drawing a conclusion based on information presented is called

• Spencer Greenberg
• Nov 26, 2018
• 11 min read

12 Ways To Draw Conclusions From Information

Updated: Sep 25, 2023

There are a LOT of ways to make inferences – that is, for drawing conclusions based on information, evidence or data. In fact, there are many more than most people realize. All of them have strengths and weaknesses that render them more useful in some situations than in others.

Here's a brief key describing most popular methods of inference, to help you whenever you're trying to draw a conclusion for yourself. Do you rely more on some of these than you should, given their weaknesses? Are there others in this list that you could benefit from using more in your life, given their strengths? And what does drawing conclusions mean, really? As you'll learn in a moment, it encompasses a wide variety of techniques, so there isn't one single definition.

1. Deduction

Common in: philosophy, mathematics

If X, then Y, due to the definitions of X and Y.

X applies to this case.

Therefore Y applies to this case.

Example: “Plato is a mortal, and all mortals are, by definition, able to die; therefore Plato is able to die.”

Example: “For any number that is an integer, there exists another integer greater than that number. 1,000,000 is an integer. So there exists an integer greater than 1,000,000.”

Advantages: When you use deduction properly in an appropriate context, it is an airtight form of inference (e.g. in a mathematical proof with no mistakes).

Flaws: To apply deduction to the world, you need to rely on strong assumptions about how the world works, or else apply other methods of inference on top. So its range of applicability is limited.

2. Frequencies

Common in: applied statistics, data science

95% of the time that X occurred in the past, Y occurred also.

X occurred.

Therefore Y is likely to occur (with high probability).

Example: “95% of the time when we saw a bank transaction identical to this one, it was fraudulent. So this transaction is fraudulent.”

Advantages: This technique allows you to assign probabilities to events. When you have a lot of past data it can be easy to apply.

Flaws: You need to have a moderately large number of examples like the current one to perform calculations on. Also, the method assumes that those past examples were drawn from a process that is (statistically) just like the one that generated this latest example. Moreover, it is unclear sometimes what it means for “X”, the type of event you’re interested in, to have occurred. What if something that’s very similar to but not quite like X occurred? Should that be counted as X occurring? If we broaden our class of what counts as X or change to another class of event that still encompasses all of our prior examples, we’ll potentially get a different answer. Fortunately, there are plenty of opportunities to make inferences from frequencies where the correct class to use is fairly obvious.

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Common in : financial engineering, risk modeling, environmental science

Given our probabilistic model of this thing, when X occurs, the probability of Y occurring is 0.95.

Example: “Given our multivariate Gaussian model of loan prices, when this loan defaults there is a 0.95 probability of this other loan defaulting.”

Example: "When we run the weather simulation model many times with randomization of the initial conditions, rain occurs tomorrow in that region 95% of the time."

Advantages: This technique can be used to make predictions in very complex scenarios (e.g. involving more variables than a human mind can take into account at once) as long as the dynamics of the systems underlying those scenarios are sufficiently well understood.

Flaws: This method hinges on the appropriateness of the model chosen; it may require a large amount of past data to estimate free model parameters, and may go haywire if modeling assumptions are unrealistic or suddenly violated by changes in the world. You may have to already understand the system deeply to be able to build the model in the first place (e.g. with weather modeling).

4. Classification

Common in: machine learning, data science

In prior data, as X1 and X2 increased, the likelihood of Y increased.

X1 and X2 are at high levels.

Therefore Y is likely to occur.

Example: “Height for children can be approximately predicted as an (increasing) linear function of age (X1) and weight (X2). This child is older and heavier than the others, so we predict he is likely to be tall.”

Example: "We've trained a neural network to predict whether a particular batch of concrete will be strong based on its constituents, mixture proportion, compaction, etc."

Advantages: This method can often produce accurate predictions for systems that you don't have much understanding of, as long as enough data is available to train the regression algorithm and that data contains sufficiently relevant variables.

Flaws: This method is often applied with simple assumptions (e.g. linearity) that may not capture the complexity of the inference problem, but very large amounts of data may be needed to apply much more complex models (e.g to use neural networks, which are non-linear). Regression also may produce results that are hard to interpret – you may not really understand why it does a good job of making predictions.

5. Bayesianism

Common in: the rationality community

Given my prior odds that Y is true...

And given evidence X...

And given my Bayes factor, which is my estimate of how much more likely X is to occur if Y is true than if Y is not true...

I calculate that Y is far more likely to be true than to not be true (by multiplying the prior odds by the Bayes factor to get the posterior odds).

Therefore Y is likely to be true (with high probability).

Example: “My prior odds that my boss is angry at me were 1 to 4, because he’s angry at me about 20% of the time. But then he came into my office shouting and flipped over my desk, which I estimate is 200 times more likely to occur if he’s angry at me compared to if he’s not. So now the odds of him being angry at me are 200 * (1/4) = 50 to 1 in favor of him being angry.”

Example: "Historically, companies in this situation have 2 to 1 odds of defaulting on their loans. But then evidence came out about this specific company showing that it is 3 times more likely to end up defaulting on its loans than similar companies. Hence now the odds of it defaulting are 6 to 1 since: (2/1) * (3/1) = 6. That means there is an 85% chance that it defaults since 0.85 = 6/(6+1)."

Advantages: If you can do the calculations in a given instance, and have a sensible way to set your prior probabilities, this is probably the mathematically optimal framework to use for probabilistic prediction. For instance, if you have a belief about the probability of something, then you gain some new evidence, you can prove mathematically that Bayes's rule tells you how to calculate what your new probability should now be that incorporates that evidence. In that sense, we can think of many of the other approaches on this list as (hopefully pragmatic) approximations of Bayesianism (sometimes good approximations, sometimes bad ones).

Flaws: It's sometimes hard to know how to set your prior odds, and it can be very hard in some cases to perform the Bayesian calculation. In practice, carrying out the calculation might end up relying on subjective estimates of the odds, which can be especially tricky to guess when the evidence is not binary (i.e not of the form “happened” vs. “didn’t happen”), or if you have lots of different pieces of evidence that are partially correlated.

If you’d like to learn more about using Bayesian inference in everyday life, try our mini-course on The Question of Evidence . For a more math-oriented explanation, check out our course on Understanding Bayes’s Theorem .

6. Theories

Common in: psychology, economics

Given our theory, when X occurs, Y occurs.

Therefore Y will occur.

Example: “One theory is that depressed people are most at risk for suicide when they are beginning to come out of a really bad depression. So as depression is remitting, patients should be carefully screened for potentially increasing suicide risk factors.”

Example: “A common theory is that when inflation rises, unemployment falls. Inflation is rising, so we should predict that unemployment will fall.”

Advantages: Theories can make systems far more understandable to the human mind, and can be taught to others. Sometimes even very complex systems can be pretty well approximated with a simple theory. Theories allow us to make predictions about what will happen while only having to focus on a small amount of relevant information, without being bogged down by thousands of details.

Flaws: It can be very challenging to come up with reliable theories, and often you will not know how accurate such a theory is. Even if it has substantial truth to it and is right often, there may be cases where the opposite of what was predicted actually happens, and for reasons the theory can’t explain. Theories usually only capture part of what is going on in a particular situation, ignoring many variables so as to be more understandable. People often get too attached to particular theories, forgetting that theories are only approximations of reality, and so pretty much always have exceptions.

Common in: engineering, biology, physics

We know that X causes Y to occur.

Example: “Rusting of gears causes increased friction, leading to greater wear and tear. In this case, the gears were heavily rusted, so we expect to find a lot of wear.”

Example: “This gene produces this phenotype, and we see that this gene is present, so we expect to see the phenotype in the offspring.”

Advantages: If you understand the causal structure of a system, you may be able to make many powerful predictions about it, including predicting what would happen in many hypothetical situations that have never occurred before, and predicting what would happen if you were to intervene on the system in a particular way. This contrasts with (probabilistic) models that may be able to accurately predict what happens in common situations, but perform badly at predicting what will happen in novel situations and in situations where you intervene on the system (e.g. what would happen to the system if I purposely changed X).

Flaws: It’s often extremely hard to figure out causality in a highly complex system, especially in “softer” or "messier" subjects like nutrition and the social sciences. Purely statistical information (even an infinite amount of it) is not enough on its own to fully describe the causality of a system; additional assumptions need to be added. Often in practice we can only answer questions about causality by running randomized experiments (e.g. randomized controlled trials), which are typically expensive and sometimes infeasible, or by attempting to carefully control for all the potential confounding variables, a challenging and error-prone process.

Common in: politics, economics

This expert (or prediction market, or prediction algorithm) X is 90% accurate at predicting things in this general domain of prediction.

X predicts Y.

Example: “This prediction market has been right 90% of the time when predicting recent baseball outcomes, and in this case predicts the Yankees will win.”

Advantages: If you can find an expert or algorithm that has been proven to make reliable predictions in a particular domain, you can simply use these predictions yourself without even understanding how they are made.

Flaws: We often don’t have access to the predictions of experts (or of prediction markets, or prediction algorithms), and when we do, we usually don’t have reliable measures of their past accuracy. What's more, many experts whose predictions are publicly available have no clear track record of performance, or even purposely avoid accountability for poor performance (e.g. by hiding past prediction failures and touting past successes).

9. Metaphors

Common in: self-help, ancient philosophy, science education

X, which is what we are dealing with now, is metaphorically a Z.

For Z, when W is true, then obviously Y is true.

Now W (or its metaphorical equivalent) is true for X.

Therefore Y is true for X.

Example: “Your life is but a boat, and you are riding on the waves of your experiences. When a raging storm hits, a boat can’t be under full sail. It can’t continue at its maximum speed. You are experiencing a storm now, and so you too must learn to slow down.”

Example: "To better understand the nature of gasses, imagine tons of ping pong balls all shooting around in straight lines in random directions, and bouncing off of each other whenever they collide. These ping pong balls represent molecules of gas. Assuming the system is not inside a container, ping pong balls at the edges of the system have nothing to collide with, so they just fly outward, expanding the whole system. Similarly, the volume of a gas expands when it is placed in a vacuum."

Advantages: Our brains are good at understanding metaphors, so they can save us mental energy when we try to grasp difficult concepts. If the two items being compared in the metaphor are sufficiently alike in relevant ways, then the metaphor may accurately reveal elements of how its subject works.

Flaws: Z working as a metaphor for X doesn’t mean that all (or even most) predictions that are accurate for situations involving Z are appropriate (or even make any sense) for X. Metaphor-based reasoning can seem profound and persuasive even in cases when it makes little sense.

10. Similarities

Common in: the study of history, machine learning

X occurred, and X is very similar to Z in properties A, B and C.

When things similar to Z in properties A, B, and C occur, Y usually occurs.

Example: “This conflict is similar to the Gulf War in various ways, and from what we've learned about wars like the Gulf War, we can expect these sorts of outcomes.”

Example: “This data point (with unknown label) is closest in feature space to this other data point which is labeled ‘cat’, and all the other labeled points around that point are also labeled ‘cat’, so this unlabeled point should also likely get the label ‘cat’.”

Advantages: This approach can be applied at both small scale (with small numbers of examples) and at large scale (with millions of examples, as in machine learning algorithms), though of course large numbers of examples tend to produce more robust results. It can be viewed as a more powerful generalization of "frequencies"-based reasoning.

Flaws: In the history case, it is difficult to know which features are the appropriate ones to use to evaluate the similarity of two cases, and often the conclusions this approach produces are based on a relatively small number of examples. In the machine learning case, a very large amount of data may be needed to train the model (and it still may be unclear how to measure which examples are similar to which other cases, even with a lot of data). The properties you're using to compare cases must be sufficiently relevant to the prediction being made for it to work.

11. Anecdotes

Common in: daily life

In this handful of examples (or perhaps even just one example) where X occurred, Y occurred.

Example: “The last time we took that so-called 'shortcut' home, we got stuck in traffic for an extra 45 minutes. Let's not make that mistake again.”

Example: “My friend Bob tried that supplement and said it gave him more energy. So maybe it will give me more energy too."

Advantages: Anecdotes are simple to use, and a few of them are often all we have to work with for inference.

Flaws: Unless we are in a situation with very little noise/variability, a few examples likely will not be enough to accurately generalize. For instance, a few examples is not enough to make a reliable judgement about how often something occurs.

12. Intuition

My intuition (that I may have trouble explaining) predicts that when X occurs, Y is true.

Therefore Y is true.

Example: “The tone of voice he used when he talked about his family gave me a bad vibe. My feeling is that anyone who talks about their family with that tone of voice probably does not really love them.”

Example: "I can't explain why, but I'm pretty sure he's going to win this election."

Advantages: Our intuitions can be very well honed in situations we’ve encountered many times, and that we've received feedback on (i.e. where there was some sort of answer we got about how well our intuition performed). For instance, a surgeon who has conducted thousands of heart surgeries may have very good intuitions about what to do during surgery, or about how the patient will fare, even potentially very accurate intuitions that she can't easily articulate.

Flaws: In novel situations, or in situations where we receive no feedback on how well our instincts are performing, our intuitions may be highly inaccurate (even though we may not feel any less confident about our correctness).

Do you want to learn more about drawing conclusions from data?

If you'd like to know more about when intuition is reliable, try our 7-question guide to determining when you can trust your intuition.

We also have a full podcast episode about Mental models that apply across disciplines that you may like:

Click here to access other streaming options and show notes.

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Making Inferences and Drawing Conclusions

Read with purpose and meaning.

Drawing conclusions refers to information that is implied or inferred.  This means that the information is never clearly stated.

Writers often tell you more than they say directly.  They give you hints or clues that help you "read between the lines."  Using these clues to give you a deeper understanding of your reading is called inferring . When you infer , you go beyond the surface details to see other meanings that the details suggest or imply (not stated).  When the meanings of words are not stated clearly in the context of the text, they may be implied  – that is, suggested or hinted at.  When meanings are implied, you may infer them.

Inference is just a big word that means a conclusion or judgement .  If you infer that something has happened, you do not see, hear, feel, smell, or taste the actual event.  But from what you know, it makes sense to think that it has happened.  You make inferences everyday.  Most of the time you do so without thinking about it.  Suppose you are sitting in your car stopped at a red signal light.  You hear screeching tires, then a loud crash and breaking glass.  You see nothing , but you infer that there has been a car accident.  We all know the sounds of screeching tires and a crash.  We know that these sounds almost always mean a car accident.  But there could be some other reason, and therefore another explanation, for the sounds.  Perhaps it was not an accident involving two moving vehicles.  Maybe an angry driver rammed a parked car.  Or maybe someone played the sound of a car crash from a recording.  Making inferences means choosing the most likely explanation from the facts at hand.

There are several ways to help you draw conclusions from what an author may be implying.  The following are descriptions of the various ways to aid you in reaching a conclusion.

General Sense

The meaning of a word may be implied by the general sense of its context, as the meaning of the word incarcerated is implied in the following sentence:

Murderers are usually incarcerated for longer periods of time than robbers.

You may infer the meaning of incarcerated by answering the question "What usually happens to those found guilty of murder or robbery?"  What have you inferred as the meaning of the word incarcerated ?

If you answered that they are locked up in jail, prison, or a penitentiary, you correctly inferred the meaning of incarcerated .

When the meaning of the word is not implied by the general sense of its context, it may be implied by examples.  For instance,

Those who enjoy belonging to clubs, going to parties, and inviting friends often to their homes for dinner are gregarious .

You may infer the meaning of gregarious by answering the question, "What word or words describe people who belong to clubs, go to parties a lot, and often invite friends over to their homes for dinner?"  What have you inferred as the meaning of the word gregarious ?

If you answered social or something like: "people who enjoy the company of others", you correctly inferred the meaning of gregarious .

Antonyms and Contrasts

When the meaning of a word is not implied by the general sense of its context or by examples, it may be implied by an antonym or by a contrasting thought in a context.  Antonyms are words that have opposite meanings, such as happy and sad.  For instance,

Ben is fearless, but his brother is timorous .

You may infer the meaning of timorous by answering the question, "If Ben is fearless and Jim is very different from Ben with regard to fear, then what word describes Jim?"

If you answered a word such as timid , or afraid , or fearful , you inferred the meaning of timorous .

A contrast in the following sentence implies the meaning of credence :

Dad gave credence to my story, but Mom's reaction was one of total disbelief.

You may infer the meaning of credence by answering the question, "If Mom's reaction was disbelief and Dad's reaction was very different from Mom's, what was Dad's reaction?"

If you answered that Dad believed the story, you correctly inferred the meaning of credence; it means belief .

Be Careful of the Meaning You Infer!

When a sentence contains an unfamiliar word, it is sometimes possible to infer the general meaning of the sentence without inferring the exact meaning of the unknown word.  For instance,

When we invite the Paulsons for dinner, they never invite us to their home for a meal; however, when we have the Browns to dinner, they always reciprocate .

In reading this sentence, some students infer that the Browns are more desirable dinner guests than the Paulsons without inferring the exact meaning of reciprocate .  Other students conclude that the Browns differ from the Paulsons in that they do something in return when they are invited for dinner; these students conclude correctly that reciprocate means "to do something in return."

In drawing conclusions (making inferences), you are really getting at the ultimate meaning of things – what is important, why it is important, how one event influences another, how one happening leads to another. 

Simply getting the facts in reading is not enough. You must think about what those facts mean to you.

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• Main idea | Quick guide
• Supporting ideas | Quick guide
• Meanings of words | Quick guide
• Organization | Quick guide
• Inferences | Quick guide
• Evaluation of evidence | Quick guide
• Purpose of component | Quick guide
• Relationship of ideas | Quick guide
• Fact or opinion | Quick guide
• Author's attitude | Quick guide
• Recognize similar situations | Quick guide

Draw conclusions | Quick guide

What else is likely to be true.

• "Which of the following statements about ___ is best supported by the information provided?"
• "The author of the passage would most likely draw which conclusion about ___?"
• Put it in your own words: Often you will be asked to draw a conclusion from a specific idea contained in the passage. It can be helpful to sum up the idea in your own words before considering the choices.
• Use process of elimination to get rid of conclusions that can’t be supported, until you find one that is.

Common Wrong Choice Types

• Too strong or extreme: Some incorrect choices will sound like a reasonable conclusion, but take it further than can be supported by the passage. Be wary of “extreme,” all-encompassing words like always, all, every or never .
• Out of Scope: Some choices may include a reasonable conclusion only tangentially related to the idea in the passage. Remember the conclusion you're drawing must be supported by information in the text .
• (Choice A)   Not all rock glaciers originate in the same way. A Not all rock glaciers originate in the same way.
• (Choice B)   Landslides initiate the formation of rock glaciers, then surface-creep movement follows. B Landslides initiate the formation of rock glaciers, then surface-creep movement follows.
• (Choice C)   Neither landslides nor surface-creep movement account for the formation of rock glaciers. C Neither landslides nor surface-creep movement account for the formation of rock glaciers.
• (Choice D)   While the definition and depth of rupture can be measured at rock glacier sites, the rate of movement cannot. D While the definition and depth of rupture can be measured at rock glacier sites, the rate of movement cannot.
• (Choice E)   Further study is required to determine the origins of rock glaciers. E Further study is required to determine the origins of rock glaciers.

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Online Guide to Writing and Research

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• Online Guide to Writing

Planning and Writing a Research Paper

Draw Conclusions

As a writer, you are presenting your viewpoint, opinions, evidence, etc. for others to review, so you must take on this task with maturity, courage and thoughtfulness.  Remember, you are adding to the discourse community with every research paper that you write.  This is a privilege and an opportunity to share your point of view with the world at large in an academic setting.

Because research generates further research, the conclusions you draw from your research are important. As a researcher, you depend on the integrity of the research that precedes your own efforts, and researchers depend on each other to draw valid conclusions. 

To test the validity of your conclusions, you will have to review both the content of your paper and the way in which you arrived at the content. You may ask yourself questions, such as the ones presented below, to detect any weak areas in your paper, so you can then make those areas stronger.  Notice that some of the questions relate to your process, others to your sources, and others to how you arrived at your conclusions.

Checklist for Evaluating Your Conclusions

Key takeaways.

• Because research generates further research, the conclusions you draw from your research are important.
• To test the validity of your conclusions, you will have to review both the content of your paper and the way in which you arrived at the content.

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Table of Contents: Online Guide to Writing

Chapter 1: College Writing

How Does College Writing Differ from Workplace Writing?

What Is College Writing?

Why So Much Emphasis on Writing?

Chapter 2: The Writing Process

Doing Exploratory Research

Getting from Notes to Your Draft

Introduction

Prewriting - Techniques to Get Started - Mining Your Intuition

Prewriting: Targeting Your Audience

Prewriting: Techniques to Get Started

Prewriting: Understanding Your Assignment

Rewriting: Being Your Own Critic

Rewriting: Creating a Revision Strategy

Rewriting: Getting Feedback

Rewriting: The Final Draft

Techniques to Get Started - Outlining

Techniques to Get Started - Using Systematic Techniques

Thesis Statement and Controlling Idea

Writing: Getting from Notes to Your Draft - Freewriting

Writing: Getting from Notes to Your Draft - Summarizing Your Ideas

Writing: Outlining What You Will Write

Chapter 3: Thinking Strategies

A Word About Style, Voice, and Tone

A Word About Style, Voice, and Tone: Style Through Vocabulary and Diction

Critical Strategies and Writing

Critical Strategies and Writing: Analysis

Critical Strategies and Writing: Evaluation

Critical Strategies and Writing: Persuasion

Critical Strategies and Writing: Synthesis

Developing a Paper Using Strategies

Kinds of Assignments You Will Write

Patterns for Presenting Information

Patterns for Presenting Information: Critiques

Patterns for Presenting Information: Discussing Raw Data

Patterns for Presenting Information: General-to-Specific Pattern

Patterns for Presenting Information: Problem-Cause-Solution Pattern

Patterns for Presenting Information: Specific-to-General Pattern

Patterns for Presenting Information: Summaries and Abstracts

Supporting with Research and Examples

Writing Essay Examinations

Writing Essay Examinations: Make Your Answer Relevant and Complete

Writing Essay Examinations: Organize Thinking Before Writing

Writing Essay Examinations: Read and Understand the Question

Chapter 4: The Research Process

Planning and Writing a Research Paper: Ask a Research Question

Planning and Writing a Research Paper: Cite Sources

Planning and Writing a Research Paper: Collect Evidence

Planning and Writing a Research Paper: Decide Your Point of View, or Role, for Your Research

Planning and Writing a Research Paper: Draw Conclusions

Planning and Writing a Research Paper: Find a Topic and Get an Overview

Planning and Writing a Research Paper: Manage Your Resources

Planning and Writing a Research Paper: Outline

Planning and Writing a Research Paper: Survey the Literature

Planning and Writing a Research Paper: Work Your Sources into Your Research Writing

Research Resources: Where Are Research Resources Found? - Human Resources

Research Resources: What Are Research Resources?

Research Resources: Where Are Research Resources Found?

Research Resources: Where Are Research Resources Found? - Electronic Resources

Research Resources: Where Are Research Resources Found? - Print Resources

Structuring the Research Paper: Formal Research Structure

Structuring the Research Paper: Informal Research Structure

The Nature of Research

The Research Assignment: How Should Research Sources Be Evaluated?

The Research Assignment: When Is Research Needed?

The Research Assignment: Why Perform Research?

Chapter 5: Academic Integrity

Academic Integrity

Giving Credit to Sources

Giving Credit to Sources: Copyright Laws

Giving Credit to Sources: Documentation

Giving Credit to Sources: Style Guides

Integrating Sources

Practicing Academic Integrity

Practicing Academic Integrity: Keeping Accurate Records

Practicing Academic Integrity: Managing Source Material

Practicing Academic Integrity: Managing Source Material - Paraphrasing Your Source

Practicing Academic Integrity: Managing Source Material - Quoting Your Source

Practicing Academic Integrity: Managing Source Material - Summarizing Your Sources

Types of Documentation

Types of Documentation: Bibliographies and Source Lists

Types of Documentation: Citing World Wide Web Sources

Types of Documentation: In-Text or Parenthetical Citations

Types of Documentation: In-Text or Parenthetical Citations - APA Style

Types of Documentation: In-Text or Parenthetical Citations - CSE/CBE Style

Types of Documentation: In-Text or Parenthetical Citations - Chicago Style

Types of Documentation: In-Text or Parenthetical Citations - MLA Style

Types of Documentation: Note Citations

Chapter 6: Using Library Resources

Finding Library Resources

Chapter 7: Assessing Your Writing

How Is Writing Graded?

How Is Writing Graded?: A General Assessment Tool

The Draft Stage

The Draft Stage: The First Draft

The Draft Stage: The Revision Process and the Final Draft

The Draft Stage: Using Feedback

The Research Stage

Using Assessment to Improve Your Writing

Chapter 8: Other Frequently Assigned Papers

Reviews and Reaction Papers: Article and Book Reviews

Reviews and Reaction Papers: Reaction Papers

Writing Arguments

Writing Arguments: Adapting the Argument Structure

Writing Arguments: Purposes of Argument

Writing Arguments: References to Consult for Writing Arguments

Writing Arguments: Steps to Writing an Argument - Anticipate Active Opposition

Writing Arguments: Steps to Writing an Argument - Determine Your Organization

Writing Arguments: Steps to Writing an Argument - Develop Your Argument

Writing Arguments: Steps to Writing an Argument - Introduce Your Argument

Writing Arguments: Steps to Writing an Argument - State Your Thesis or Proposition

Writing Arguments: Steps to Writing an Argument - Write Your Conclusion

Writing Arguments: Types of Argument

Appendix A: Books to Help Improve Your Writing

Dictionaries

General Style Manuals

Researching on the Internet

Special Style Manuals

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Appendix B: Collaborative Writing and Peer Reviewing

Collaborative Writing: Assignments to Accompany the Group Project

Collaborative Writing: Informal Progress Report

Collaborative Writing: Issues to Resolve

Collaborative Writing: Methodology

Collaborative Writing: Peer Evaluation

Collaborative Writing: Tasks of Collaborative Writing Group Members

Collaborative Writing: Writing Plan

General Introduction

Peer Reviewing

Appendix C: Developing an Improvement Plan

Working with Your Instructor’s Comments and Grades

Appendix D: Writing Plan and Project Schedule

Devising a Writing Project Plan and Schedule

Reviewing Your Plan with Others

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• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• 9. The Conclusion
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

The conclusion is intended to help the reader understand why your research should matter to them after they have finished reading the paper. A conclusion is not merely a summary of the main topics covered or a re-statement of your research problem, but a synthesis of key points derived from the findings of your study and, if applicable, where you recommend new areas for future research. For most college-level research papers, two or three well-developed paragraphs is sufficient for a conclusion, although in some cases, more paragraphs may be required in describing the key findings and their significance.

Conclusions. The Writing Center. University of North Carolina; Conclusions. The Writing Lab and The OWL. Purdue University.

Importance of a Good Conclusion

A well-written conclusion provides you with important opportunities to demonstrate to the reader your understanding of the research problem. These include:

• Presenting the last word on the issues you raised in your paper . Just as the introduction gives a first impression to your reader, the conclusion offers a chance to leave a lasting impression. Do this, for example, by highlighting key findings in your analysis that advance new understanding about the research problem, that are unusual or unexpected, or that have important implications applied to practice.
• Summarizing your thoughts and conveying the larger significance of your study . The conclusion is an opportunity to succinctly re-emphasize  your answer to the "So What?" question by placing the study within the context of how your research advances past research about the topic.
• Identifying how a gap in the literature has been addressed . The conclusion can be where you describe how a previously identified gap in the literature [first identified in your literature review section] has been addressed by your research and why this contribution is significant.
• Demonstrating the importance of your ideas . Don't be shy. The conclusion offers an opportunity to elaborate on the impact and significance of your findings. This is particularly important if your study approached examining the research problem from an unusual or innovative perspective.
• Introducing possible new or expanded ways of thinking about the research problem . This does not refer to introducing new information [which should be avoided], but to offer new insight and creative approaches for framing or contextualizing the research problem based on the results of your study.

Bunton, David. “The Structure of PhD Conclusion Chapters.” Journal of English for Academic Purposes 4 (July 2005): 207–224; Conclusions. The Writing Center. University of North Carolina; Kretchmer, Paul. Twelve Steps to Writing an Effective Conclusion. San Francisco Edit, 2003-2008; Conclusions. The Writing Lab and The OWL. Purdue University; Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8.

Structure and Writing Style

I.  General Rules

The general function of your paper's conclusion is to restate the main argument . It reminds the reader of the strengths of your main argument(s) and reiterates the most important evidence supporting those argument(s). Do this by clearly summarizing the context, background, and necessity of pursuing the research problem you investigated in relation to an issue, controversy, or a gap found in the literature. However, make sure that your conclusion is not simply a repetitive summary of the findings. This reduces the impact of the argument(s) you have developed in your paper.

When writing the conclusion to your paper, follow these general rules:

• Present your conclusions in clear, concise language. Re-state the purpose of your study, then describe how your findings differ or support those of other studies and why [i.e., what were the unique, new, or crucial contributions your study made to the overall research about your topic?].
• Do not simply reiterate your findings or the discussion of your results. Provide a synthesis of arguments presented in the paper to show how these converge to address the research problem and the overall objectives of your study.
• Indicate opportunities for future research if you haven't already done so in the discussion section of your paper. Highlighting the need for further research provides the reader with evidence that you have an in-depth awareness of the research problem but that further investigations should take place beyond the scope of your investigation.

Consider the following points to help ensure your conclusion is presented well:

• If the argument or purpose of your paper is complex, you may need to summarize the argument for your reader.
• If, prior to your conclusion, you have not yet explained the significance of your findings or if you are proceeding inductively, use the end of your paper to describe your main points and explain their significance.
• Move from a detailed to a general level of consideration that returns the topic to the context provided by the introduction or within a new context that emerges from the data [this is opposite of the introduction, which begins with general discussion of the context and ends with a detailed description of the research problem]. 

The conclusion also provides a place for you to persuasively and succinctly restate the research problem, given that the reader has now been presented with all the information about the topic . Depending on the discipline you are writing in, the concluding paragraph may contain your reflections on the evidence presented. However, the nature of being introspective about the research you have conducted will depend on the topic and whether your professor wants you to express your observations in this way. If asked to think introspectively about the topics, do not delve into idle speculation. Being introspective means looking within yourself as an author to try and understand an issue more deeply, not to guess at possible outcomes or make up scenarios not supported by the evidence.

II.  Developing a Compelling Conclusion

Although an effective conclusion needs to be clear and succinct, it does not need to be written passively or lack a compelling narrative. Strategies to help you move beyond merely summarizing the key points of your research paper may include any of the following:

• If your essay deals with a critical, contemporary problem, warn readers of the possible consequences of not attending to the problem proactively.
• Recommend a specific course or courses of action that, if adopted, could address a specific problem in practice or in the development of new knowledge leading to positive change.
• Cite a relevant quotation or expert opinion already noted in your paper in order to lend authority and support to the conclusion(s) you have reached [a good source would be from your literature review].
• Explain the consequences of your research in a way that elicits action or demonstrates urgency in seeking change.
• Restate a key statistic, fact, or visual image to emphasize the most important finding of your paper.
• If your discipline encourages personal reflection, illustrate your concluding point by drawing from your own life experiences.
• Return to an anecdote, an example, or a quotation that you presented in your introduction, but add further insight derived from the findings of your study; use your interpretation of results from your study to recast it in new or important ways.
• Provide a "take-home" message in the form of a succinct, declarative statement that you want the reader to remember about your study.

III. Problems to Avoid

Failure to be concise Your conclusion section should be concise and to the point. Conclusions that are too lengthy often have unnecessary information in them. The conclusion is not the place for details about your methodology or results. Although you should give a summary of what was learned from your research, this summary should be relatively brief, since the emphasis in the conclusion is on the implications, evaluations, insights, and other forms of analysis that you make. Strategies for writing concisely can be found here .

Failure to comment on larger, more significant issues In the introduction, your task was to move from the general [the field of study] to the specific [the research problem]. However, in the conclusion, your task is to move from a specific discussion [your research problem] back to a general discussion framed around the implications and significance of your findings [i.e., how your research contributes new understanding or fills an important gap in the literature]. In short, the conclusion is where you should place your research within a larger context [visualize your paper as an hourglass--start with a broad introduction and review of the literature, move to the specific analysis and discussion, conclude with a broad summary of the study's implications and significance].

Failure to reveal problems and negative results Negative aspects of the research process should never be ignored. These are problems, deficiencies, or challenges encountered during your study. They should be summarized as a way of qualifying your overall conclusions. If you encountered negative or unintended results [i.e., findings that are validated outside the research context in which they were generated], you must report them in the results section and discuss their implications in the discussion section of your paper. In the conclusion, use negative results as an opportunity to explain their possible significance and/or how they may form the basis for future research.

Failure to provide a clear summary of what was learned In order to be able to discuss how your research fits within your field of study [and possibly the world at large], you need to summarize briefly and succinctly how it contributes to new knowledge or a new understanding about the research problem. This element of your conclusion may be only a few sentences long.

Failure to match the objectives of your research Often research objectives in the social and behavioral sciences change while the research is being carried out. This is not a problem unless you forget to go back and refine the original objectives in your introduction. As these changes emerge they must be documented so that they accurately reflect what you were trying to accomplish in your research [not what you thought you might accomplish when you began].

Resist the urge to apologize If you've immersed yourself in studying the research problem, you presumably should know a good deal about it [perhaps even more than your professor!]. Nevertheless, by the time you have finished writing, you may be having some doubts about what you have produced. Repress those doubts! Don't undermine your authority as a researcher by saying something like, "This is just one approach to examining this problem; there may be other, much better approaches that...." The overall tone of your conclusion should convey confidence to the reader about the study's validity and realiability.

Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8; Concluding Paragraphs. College Writing Center at Meramec. St. Louis Community College; Conclusions. The Writing Center. University of North Carolina; Conclusions. The Writing Lab and The OWL. Purdue University; Freedman, Leora  and Jerry Plotnick. Introductions and Conclusions. The Lab Report. University College Writing Centre. University of Toronto; Leibensperger, Summer. Draft Your Conclusion. Academic Center, the University of Houston-Victoria, 2003; Make Your Last Words Count. The Writer’s Handbook. Writing Center. University of Wisconsin Madison; Miquel, Fuster-Marquez and Carmen Gregori-Signes. “Chapter Six: ‘Last but Not Least:’ Writing the Conclusion of Your Paper.” In Writing an Applied Linguistics Thesis or Dissertation: A Guide to Presenting Empirical Research . John Bitchener, editor. (Basingstoke,UK: Palgrave Macmillan, 2010), pp. 93-105; Tips for Writing a Good Conclusion. Writing@CSU. Colorado State University; Kretchmer, Paul. Twelve Steps to Writing an Effective Conclusion. San Francisco Edit, 2003-2008; Writing Conclusions. Writing Tutorial Services, Center for Innovative Teaching and Learning. Indiana University; Writing: Considering Structure and Organization. Institute for Writing Rhetoric. Dartmouth College.

Writing Tip

Don't Belabor the Obvious!

Avoid phrases like "in conclusion...," "in summary...," or "in closing...." These phrases can be useful, even welcome, in oral presentations. But readers can see by the tell-tale section heading and number of pages remaining that they are reaching the end of your paper. You'll irritate your readers if you belabor the obvious.

Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8.

Another Writing Tip

New Insight, Not New Information!

Don't surprise the reader with new information in your conclusion that was never referenced anywhere else in the paper. This why the conclusion rarely has citations to sources. If you have new information to present, add it to the discussion or other appropriate section of the paper. Note that, although no new information is introduced, the conclusion, along with the discussion section, is where you offer your most "original" contributions in the paper; the conclusion is where you describe the value of your research, demonstrate that you understand the material that you’ve presented, and position your findings within the larger context of scholarship on the topic, including describing how your research contributes new insights to that scholarship.

Assan, Joseph. "Writing the Conclusion Chapter: The Good, the Bad and the Missing." Liverpool: Development Studies Association (2009): 1-8; Conclusions. The Writing Center. University of North Carolina.

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Conclusions

What this handout is about.

This handout will explain the functions of conclusions, offer strategies for writing effective ones, help you evaluate conclusions you’ve drafted, and suggest approaches to avoid.

About conclusions

Introductions and conclusions can be difficult to write, but they’re worth investing time in. They can have a significant influence on a reader’s experience of your paper.

Just as your introduction acts as a bridge that transports your readers from their own lives into the “place” of your analysis, your conclusion can provide a bridge to help your readers make the transition back to their daily lives. Such a conclusion will help them see why all your analysis and information should matter to them after they put the paper down.

Your conclusion is your chance to have the last word on the subject. The conclusion allows you to have the final say on the issues you have raised in your paper, to synthesize your thoughts, to demonstrate the importance of your ideas, and to propel your reader to a new view of the subject. It is also your opportunity to make a good final impression and to end on a positive note.

Your conclusion can go beyond the confines of the assignment. The conclusion pushes beyond the boundaries of the prompt and allows you to consider broader issues, make new connections, and elaborate on the significance of your findings.

Your conclusion should make your readers glad they read your paper. Your conclusion gives your reader something to take away that will help them see things differently or appreciate your topic in personally relevant ways. It can suggest broader implications that will not only interest your reader, but also enrich your reader’s life in some way. It is your gift to the reader.

Strategies for writing an effective conclusion

One or more of the following strategies may help you write an effective conclusion:

• Play the “So What” Game. If you’re stuck and feel like your conclusion isn’t saying anything new or interesting, ask a friend to read it with you. Whenever you make a statement from your conclusion, ask the friend to say, “So what?” or “Why should anybody care?” Then ponder that question and answer it. Here’s how it might go: You: Basically, I’m just saying that education was important to Douglass. Friend: So what? You: Well, it was important because it was a key to him feeling like a free and equal citizen. Friend: Why should anybody care? You: That’s important because plantation owners tried to keep slaves from being educated so that they could maintain control. When Douglass obtained an education, he undermined that control personally. You can also use this strategy on your own, asking yourself “So What?” as you develop your ideas or your draft.
• Return to the theme or themes in the introduction. This strategy brings the reader full circle. For example, if you begin by describing a scenario, you can end with the same scenario as proof that your essay is helpful in creating a new understanding. You may also refer to the introductory paragraph by using key words or parallel concepts and images that you also used in the introduction.
• Synthesize, don’t summarize. Include a brief summary of the paper’s main points, but don’t simply repeat things that were in your paper. Instead, show your reader how the points you made and the support and examples you used fit together. Pull it all together.
• Include a provocative insight or quotation from the research or reading you did for your paper.
• Propose a course of action, a solution to an issue, or questions for further study. This can redirect your reader’s thought process and help them to apply your info and ideas to their own life or to see the broader implications.
• Point to broader implications. For example, if your paper examines the Greensboro sit-ins or another event in the Civil Rights Movement, you could point out its impact on the Civil Rights Movement as a whole. A paper about the style of writer Virginia Woolf could point to her influence on other writers or on later feminists.

Strategies to avoid

• Beginning with an unnecessary, overused phrase such as “in conclusion,” “in summary,” or “in closing.” Although these phrases can work in speeches, they come across as wooden and trite in writing.
• Stating the thesis for the very first time in the conclusion.
• Introducing a new idea or subtopic in your conclusion.
• Ending with a rephrased thesis statement without any substantive changes.
• Making sentimental, emotional appeals that are out of character with the rest of an analytical paper.
• Including evidence (quotations, statistics, etc.) that should be in the body of the paper.

Four kinds of ineffective conclusions

• The “That’s My Story and I’m Sticking to It” Conclusion. This conclusion just restates the thesis and is usually painfully short. It does not push the ideas forward. People write this kind of conclusion when they can’t think of anything else to say. Example: In conclusion, Frederick Douglass was, as we have seen, a pioneer in American education, proving that education was a major force for social change with regard to slavery.
• The “Sherlock Holmes” Conclusion. Sometimes writers will state the thesis for the very first time in the conclusion. You might be tempted to use this strategy if you don’t want to give everything away too early in your paper. You may think it would be more dramatic to keep the reader in the dark until the end and then “wow” them with your main idea, as in a Sherlock Holmes mystery. The reader, however, does not expect a mystery, but an analytical discussion of your topic in an academic style, with the main argument (thesis) stated up front. Example: (After a paper that lists numerous incidents from the book but never says what these incidents reveal about Douglass and his views on education): So, as the evidence above demonstrates, Douglass saw education as a way to undermine the slaveholders’ power and also an important step toward freedom.
• The “America the Beautiful”/”I Am Woman”/”We Shall Overcome” Conclusion. This kind of conclusion usually draws on emotion to make its appeal, but while this emotion and even sentimentality may be very heartfelt, it is usually out of character with the rest of an analytical paper. A more sophisticated commentary, rather than emotional praise, would be a more fitting tribute to the topic. Example: Because of the efforts of fine Americans like Frederick Douglass, countless others have seen the shining beacon of light that is education. His example was a torch that lit the way for others. Frederick Douglass was truly an American hero.
• The “Grab Bag” Conclusion. This kind of conclusion includes extra information that the writer found or thought of but couldn’t integrate into the main paper. You may find it hard to leave out details that you discovered after hours of research and thought, but adding random facts and bits of evidence at the end of an otherwise-well-organized essay can just create confusion. Example: In addition to being an educational pioneer, Frederick Douglass provides an interesting case study for masculinity in the American South. He also offers historians an interesting glimpse into slave resistance when he confronts Covey, the overseer. His relationships with female relatives reveal the importance of family in the slave community.

Works consulted

We consulted these works while writing this handout. This is not a comprehensive list of resources on the handout’s topic, and we encourage you to do your own research to find additional publications. Please do not use this list as a model for the format of your own reference list, as it may not match the citation style you are using. For guidance on formatting citations, please see the UNC Libraries citation tutorial . We revise these tips periodically and welcome feedback.

Douglass, Frederick. 1995. Narrative of the Life of Frederick Douglass, an American Slave, Written by Himself. New York: Dover.

Hamilton College. n.d. “Conclusions.” Writing Center. Accessed June 14, 2019. https://www.hamilton.edu//academics/centers/writing/writing-resources/conclusions .

Holewa, Randa. 2004. “Strategies for Writing a Conclusion.” LEO: Literacy Education Online. Last updated February 19, 2004. https://leo.stcloudstate.edu/acadwrite/conclude.html.

You may reproduce it for non-commercial use if you use the entire handout and attribute the source: The Writing Center, University of North Carolina at Chapel Hill

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Cochrane Training

Chapter 15: interpreting results and drawing conclusions.

Holger J Schünemann, Gunn E Vist, Julian PT Higgins, Nancy Santesso, Jonathan J Deeks, Paul Glasziou, Elie A Akl, Gordon H Guyatt; on behalf of the Cochrane GRADEing Methods Group

Key Points:

• This chapter provides guidance on interpreting the results of synthesis in order to communicate the conclusions of the review effectively.
• Methods are presented for computing, presenting and interpreting relative and absolute effects for dichotomous outcome data, including the number needed to treat (NNT).
• For continuous outcome measures, review authors can present summary results for studies using natural units of measurement or as minimal important differences when all studies use the same scale. When studies measure the same construct but with different scales, review authors will need to find a way to interpret the standardized mean difference, or to use an alternative effect measure for the meta-analysis such as the ratio of means.
• Review authors should not describe results as ‘statistically significant’, ‘not statistically significant’ or ‘non-significant’ or unduly rely on thresholds for P values, but report the confidence interval together with the exact P value.
• Review authors should not make recommendations about healthcare decisions, but they can – after describing the certainty of evidence and the balance of benefits and harms – highlight different actions that might be consistent with particular patterns of values and preferences and other factors that determine a decision such as cost.

Cite this chapter as: Schünemann HJ, Vist GE, Higgins JPT, Santesso N, Deeks JJ, Glasziou P, Akl EA, Guyatt GH. Chapter 15: Interpreting results and drawing conclusions. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.4 (updated August 2023). Cochrane, 2023. Available from www.training.cochrane.org/handbook .

15.1 Introduction

The purpose of Cochrane Reviews is to facilitate healthcare decisions by patients and the general public, clinicians, guideline developers, administrators and policy makers. They also inform future research. A clear statement of findings, a considered discussion and a clear presentation of the authors’ conclusions are, therefore, important parts of the review. In particular, the following issues can help people make better informed decisions and increase the usability of Cochrane Reviews:

• information on all important outcomes, including adverse outcomes;
• the certainty of the evidence for each of these outcomes, as it applies to specific populations and specific interventions; and
• clarification of the manner in which particular values and preferences may bear on the desirable and undesirable consequences of the intervention.

A ‘Summary of findings’ table, described in Chapter 14 , Section 14.1 , provides key pieces of information about health benefits and harms in a quick and accessible format. It is highly desirable that review authors include a ‘Summary of findings’ table in Cochrane Reviews alongside a sufficient description of the studies and meta-analyses to support its contents. This description includes the rating of the certainty of evidence, also called the quality of the evidence or confidence in the estimates of the effects, which is expected in all Cochrane Reviews.

‘Summary of findings’ tables are usually supported by full evidence profiles which include the detailed ratings of the evidence (Guyatt et al 2011a, Guyatt et al 2013a, Guyatt et al 2013b, Santesso et al 2016). The Discussion section of the text of the review provides space to reflect and consider the implications of these aspects of the review’s findings. Cochrane Reviews include five standard subheadings to ensure the Discussion section places the review in an appropriate context: ‘Summary of main results (benefits and harms)’; ‘Potential biases in the review process’; ‘Overall completeness and applicability of evidence’; ‘Certainty of the evidence’; and ‘Agreements and disagreements with other studies or reviews’. Following the Discussion, the Authors’ conclusions section is divided into two standard subsections: ‘Implications for practice’ and ‘Implications for research’. The assessment of the certainty of evidence facilitates a structured description of the implications for practice and research.

Because Cochrane Reviews have an international audience, the Discussion and Authors’ conclusions should, so far as possible, assume a broad international perspective and provide guidance for how the results could be applied in different settings, rather than being restricted to specific national or local circumstances. Cultural differences and economic differences may both play an important role in determining the best course of action based on the results of a Cochrane Review. Furthermore, individuals within societies have widely varying values and preferences regarding health states, and use of societal resources to achieve particular health states. For all these reasons, and because information that goes beyond that included in a Cochrane Review is required to make fully informed decisions, different people will often make different decisions based on the same evidence presented in a review.

Thus, review authors should avoid specific recommendations that inevitably depend on assumptions about available resources, values and preferences, and other factors such as equity considerations, feasibility and acceptability of an intervention. The purpose of the review should be to present information and aid interpretation rather than to offer recommendations. The discussion and conclusions should help people understand the implications of the evidence in relation to practical decisions and apply the results to their specific situation. Review authors can aid this understanding of the implications by laying out different scenarios that describe certain value structures.

In this chapter, we address first one of the key aspects of interpreting findings that is also fundamental in completing a ‘Summary of findings’ table: the certainty of evidence related to each of the outcomes. We then provide a more detailed consideration of issues around applicability and around interpretation of numerical results, and provide suggestions for presenting authors’ conclusions.

15.2 Issues of indirectness and applicability

15.2.1 the role of the review author.

“A leap of faith is always required when applying any study findings to the population at large” or to a specific person. “In making that jump, one must always strike a balance between making justifiable broad generalizations and being too conservative in one’s conclusions” (Friedman et al 1985). In addition to issues about risk of bias and other domains determining the certainty of evidence, this leap of faith is related to how well the identified body of evidence matches the posed PICO ( Population, Intervention, Comparator(s) and Outcome ) question. As to the population, no individual can be entirely matched to the population included in research studies. At the time of decision, there will always be differences between the study population and the person or population to whom the evidence is applied; sometimes these differences are slight, sometimes large.

The terms applicability, generalizability, external validity and transferability are related, sometimes used interchangeably and have in common that they lack a clear and consistent definition in the classic epidemiological literature (Schünemann et al 2013). However, all of the terms describe one overarching theme: whether or not available research evidence can be directly used to answer the health and healthcare question at hand, ideally supported by a judgement about the degree of confidence in this use (Schünemann et al 2013). GRADE’s certainty domains include a judgement about ‘indirectness’ to describe all of these aspects including the concept of direct versus indirect comparisons of different interventions (Atkins et al 2004, Guyatt et al 2008, Guyatt et al 2011b).

To address adequately the extent to which a review is relevant for the purpose to which it is being put, there are certain things the review author must do, and certain things the user of the review must do to assess the degree of indirectness. Cochrane and the GRADE Working Group suggest using a very structured framework to address indirectness. We discuss here and in Chapter 14 what the review author can do to help the user. Cochrane Review authors must be extremely clear on the population, intervention and outcomes that they intend to address. Chapter 14, Section 14.1.2 , also emphasizes a crucial step: the specification of all patient-important outcomes relevant to the intervention strategies under comparison.

In considering whether the effect of an intervention applies equally to all participants, and whether different variations on the intervention have similar effects, review authors need to make a priori hypotheses about possible effect modifiers, and then examine those hypotheses (see Chapter 10, Section 10.10 and Section 10.11 ). If they find apparent subgroup effects, they must ultimately decide whether or not these effects are credible (Sun et al 2012). Differences between subgroups, particularly those that correspond to differences between studies, should be interpreted cautiously. Some chance variation between subgroups is inevitable so, unless there is good reason to believe that there is an interaction, review authors should not assume that the subgroup effect exists. If, despite due caution, review authors judge subgroup effects in terms of relative effect estimates as credible (i.e. the effects differ credibly), they should conduct separate meta-analyses for the relevant subgroups, and produce separate ‘Summary of findings’ tables for those subgroups.

The user of the review will be challenged with ‘individualization’ of the findings, whether they seek to apply the findings to an individual patient or a policy decision in a specific context. For example, even if relative effects are similar across subgroups, absolute effects will differ according to baseline risk. Review authors can help provide this information by identifying identifiable groups of people with varying baseline risks in the ‘Summary of findings’ tables, as discussed in Chapter 14, Section 14.1.3 . Users can then identify their specific case or population as belonging to a particular risk group, if relevant, and assess their likely magnitude of benefit or harm accordingly. A description of the identifying prognostic or baseline risk factors in a brief scenario (e.g. age or gender) will help users of a review further.

Another decision users must make is whether their individual case or population of interest is so different from those included in the studies that they cannot use the results of the systematic review and meta-analysis at all. Rather than rigidly applying the inclusion and exclusion criteria of studies, it is better to ask whether or not there are compelling reasons why the evidence should not be applied to a particular patient. Review authors can sometimes help decision makers by identifying important variation where divergence might limit the applicability of results (Rothwell 2005, Schünemann et al 2006, Guyatt et al 2011b, Schünemann et al 2013), including biologic and cultural variation, and variation in adherence to an intervention.

In addressing these issues, review authors cannot be aware of, or address, the myriad of differences in circumstances around the world. They can, however, address differences of known importance to many people and, importantly, they should avoid assuming that other people’s circumstances are the same as their own in discussing the results and drawing conclusions.

15.2.2 Biological variation

Issues of biological variation that may affect the applicability of a result to a reader or population include divergence in pathophysiology (e.g. biological differences between women and men that may affect responsiveness to an intervention) and divergence in a causative agent (e.g. for infectious diseases such as malaria, which may be caused by several different parasites). The discussion of the results in the review should make clear whether the included studies addressed all or only some of these groups, and whether any important subgroup effects were found.

15.2.3 Variation in context

Some interventions, particularly non-pharmacological interventions, may work in some contexts but not in others; the situation has been described as program by context interaction (Hawe et al 2004). Contextual factors might pertain to the host organization in which an intervention is offered, such as the expertise, experience and morale of the staff expected to carry out the intervention, the competing priorities for the clinician’s or staff’s attention, the local resources such as service and facilities made available to the program and the status or importance given to the program by the host organization. Broader context issues might include aspects of the system within which the host organization operates, such as the fee or payment structure for healthcare providers and the local insurance system. Some interventions, in particular complex interventions (see Chapter 17 ), can be only partially implemented in some contexts, and this requires judgements about indirectness of the intervention and its components for readers in that context (Schünemann 2013).

Contextual factors may also pertain to the characteristics of the target group or population, such as cultural and linguistic diversity, socio-economic position, rural/urban setting. These factors may mean that a particular style of care or relationship evolves between service providers and consumers that may or may not match the values and technology of the program.

For many years these aspects have been acknowledged when decision makers have argued that results of evidence reviews from other countries do not apply in their own country or setting. Whilst some programmes/interventions have been successfully transferred from one context to another, others have not (Resnicow et al 1993, Lumley et al 2004, Coleman et al 2015). Review authors should be cautious when making generalizations from one context to another. They should report on the presence (or otherwise) of context-related information in intervention studies, where this information is available.

15.2.4 Variation in adherence

Variation in the adherence of the recipients and providers of care can limit the certainty in the applicability of results. Predictable differences in adherence can be due to divergence in how recipients of care perceive the intervention (e.g. the importance of side effects), economic conditions or attitudes that make some forms of care inaccessible in some settings, such as in low-income countries (Dans et al 2007). It should not be assumed that high levels of adherence in closely monitored randomized trials will translate into similar levels of adherence in normal practice.

15.2.5 Variation in values and preferences

Decisions about healthcare management strategies and options involve trading off health benefits and harms. The right choice may differ for people with different values and preferences (i.e. the importance people place on the outcomes and interventions), and it is important that decision makers ensure that decisions are consistent with a patient or population’s values and preferences. The importance placed on outcomes, together with other factors, will influence whether the recipients of care will or will not accept an option that is offered (Alonso-Coello et al 2016) and, thus, can be one factor influencing adherence. In Section 15.6 , we describe how the review author can help this process and the limits of supporting decision making based on intervention reviews.

15.3 Interpreting results of statistical analyses

15.3.1 confidence intervals.

Results for both individual studies and meta-analyses are reported with a point estimate together with an associated confidence interval. For example, ‘The odds ratio was 0.75 with a 95% confidence interval of 0.70 to 0.80’. The point estimate (0.75) is the best estimate of the magnitude and direction of the experimental intervention’s effect compared with the comparator intervention. The confidence interval describes the uncertainty inherent in any estimate, and describes a range of values within which we can be reasonably sure that the true effect actually lies. If the confidence interval is relatively narrow (e.g. 0.70 to 0.80), the effect size is known precisely. If the interval is wider (e.g. 0.60 to 0.93) the uncertainty is greater, although there may still be enough precision to make decisions about the utility of the intervention. Intervals that are very wide (e.g. 0.50 to 1.10) indicate that we have little knowledge about the effect and this imprecision affects our certainty in the evidence, and that further information would be needed before we could draw a more certain conclusion.

A 95% confidence interval is often interpreted as indicating a range within which we can be 95% certain that the true effect lies. This statement is a loose interpretation, but is useful as a rough guide. The strictly correct interpretation of a confidence interval is based on the hypothetical notion of considering the results that would be obtained if the study were repeated many times. If a study were repeated infinitely often, and on each occasion a 95% confidence interval calculated, then 95% of these intervals would contain the true effect (see Section 15.3.3 for further explanation).

The width of the confidence interval for an individual study depends to a large extent on the sample size. Larger studies tend to give more precise estimates of effects (and hence have narrower confidence intervals) than smaller studies. For continuous outcomes, precision depends also on the variability in the outcome measurements (i.e. how widely individual results vary between people in the study, measured as the standard deviation); for dichotomous outcomes it depends on the risk of the event (more frequent events allow more precision, and narrower confidence intervals), and for time-to-event outcomes it also depends on the number of events observed. All these quantities are used in computation of the standard errors of effect estimates from which the confidence interval is derived.

The width of a confidence interval for a meta-analysis depends on the precision of the individual study estimates and on the number of studies combined. In addition, for random-effects models, precision will decrease with increasing heterogeneity and confidence intervals will widen correspondingly (see Chapter 10, Section 10.10.4 ). As more studies are added to a meta-analysis the width of the confidence interval usually decreases. However, if the additional studies increase the heterogeneity in the meta-analysis and a random-effects model is used, it is possible that the confidence interval width will increase.

Confidence intervals and point estimates have different interpretations in fixed-effect and random-effects models. While the fixed-effect estimate and its confidence interval address the question ‘what is the best (single) estimate of the effect?’, the random-effects estimate assumes there to be a distribution of effects, and the estimate and its confidence interval address the question ‘what is the best estimate of the average effect?’ A confidence interval may be reported for any level of confidence (although they are most commonly reported for 95%, and sometimes 90% or 99%). For example, the odds ratio of 0.80 could be reported with an 80% confidence interval of 0.73 to 0.88; a 90% interval of 0.72 to 0.89; and a 95% interval of 0.70 to 0.92. As the confidence level increases, the confidence interval widens.

There is logical correspondence between the confidence interval and the P value (see Section 15.3.3 ). The 95% confidence interval for an effect will exclude the null value (such as an odds ratio of 1.0 or a risk difference of 0) if and only if the test of significance yields a P value of less than 0.05. If the P value is exactly 0.05, then either the upper or lower limit of the 95% confidence interval will be at the null value. Similarly, the 99% confidence interval will exclude the null if and only if the test of significance yields a P value of less than 0.01.

Together, the point estimate and confidence interval provide information to assess the effects of the intervention on the outcome. For example, suppose that we are evaluating an intervention that reduces the risk of an event and we decide that it would be useful only if it reduced the risk of an event from 30% by at least 5 percentage points to 25% (these values will depend on the specific clinical scenario and outcomes, including the anticipated harms). If the meta-analysis yielded an effect estimate of a reduction of 10 percentage points with a tight 95% confidence interval, say, from 7% to 13%, we would be able to conclude that the intervention was useful since both the point estimate and the entire range of the interval exceed our criterion of a reduction of 5% for net health benefit. However, if the meta-analysis reported the same risk reduction of 10% but with a wider interval, say, from 2% to 18%, although we would still conclude that our best estimate of the intervention effect is that it provides net benefit, we could not be so confident as we still entertain the possibility that the effect could be between 2% and 5%. If the confidence interval was wider still, and included the null value of a difference of 0%, we would still consider the possibility that the intervention has no effect on the outcome whatsoever, and would need to be even more sceptical in our conclusions.

Review authors may use the same general approach to conclude that an intervention is not useful. Continuing with the above example where the criterion for an important difference that should be achieved to provide more benefit than harm is a 5% risk difference, an effect estimate of 2% with a 95% confidence interval of 1% to 4% suggests that the intervention does not provide net health benefit.

15.3.2 P values and statistical significance

A P value is the standard result of a statistical test, and is the probability of obtaining the observed effect (or larger) under a ‘null hypothesis’. In the context of Cochrane Reviews there are two commonly used statistical tests. The first is a test of overall effect (a Z-test), and its null hypothesis is that there is no overall effect of the experimental intervention compared with the comparator on the outcome of interest. The second is the (Chi 2 ) test for heterogeneity, and its null hypothesis is that there are no differences in the intervention effects across studies.

A P value that is very small indicates that the observed effect is very unlikely to have arisen purely by chance, and therefore provides evidence against the null hypothesis. It has been common practice to interpret a P value by examining whether it is smaller than particular threshold values. In particular, P values less than 0.05 are often reported as ‘statistically significant’, and interpreted as being small enough to justify rejection of the null hypothesis. However, the 0.05 threshold is an arbitrary one that became commonly used in medical and psychological research largely because P values were determined by comparing the test statistic against tabulations of specific percentage points of statistical distributions. If review authors decide to present a P value with the results of a meta-analysis, they should report a precise P value (as calculated by most statistical software), together with the 95% confidence interval. Review authors should not describe results as ‘statistically significant’, ‘not statistically significant’ or ‘non-significant’ or unduly rely on thresholds for P values , but report the confidence interval together with the exact P value (see MECIR Box 15.3.a ).

We discuss interpretation of the test for heterogeneity in Chapter 10, Section 10.10.2 ; the remainder of this section refers mainly to tests for an overall effect. For tests of an overall effect, the computation of P involves both the effect estimate and precision of the effect estimate (driven largely by sample size). As precision increases, the range of plausible effects that could occur by chance is reduced. Correspondingly, the statistical significance of an effect of a particular magnitude will usually be greater (the P value will be smaller) in a larger study than in a smaller study.

P values are commonly misinterpreted in two ways. First, a moderate or large P value (e.g. greater than 0.05) may be misinterpreted as evidence that the intervention has no effect on the outcome. There is an important difference between this statement and the correct interpretation that there is a high probability that the observed effect on the outcome is due to chance alone. To avoid such a misinterpretation, review authors should always examine the effect estimate and its 95% confidence interval.

The second misinterpretation is to assume that a result with a small P value for the summary effect estimate implies that an experimental intervention has an important benefit. Such a misinterpretation is more likely to occur in large studies and meta-analyses that accumulate data over dozens of studies and thousands of participants. The P value addresses the question of whether the experimental intervention effect is precisely nil; it does not examine whether the effect is of a magnitude of importance to potential recipients of the intervention. In a large study, a small P value may represent the detection of a trivial effect that may not lead to net health benefit when compared with the potential harms (i.e. harmful effects on other important outcomes). Again, inspection of the point estimate and confidence interval helps correct interpretations (see Section 15.3.1 ).

MECIR Box 15.3.a Relevant expectations for conduct of intervention reviews

15.3.3 Relation between confidence intervals, statistical significance and certainty of evidence

The confidence interval (and imprecision) is only one domain that influences overall uncertainty about effect estimates. Uncertainty resulting from imprecision (i.e. statistical uncertainty) may be no less important than uncertainty from indirectness, or any other GRADE domain, in the context of decision making (Schünemann 2016). Thus, the extent to which interpretations of the confidence interval described in Sections 15.3.1 and 15.3.2 correspond to conclusions about overall certainty of the evidence for the outcome of interest depends on these other domains. If there are no concerns about other domains that determine the certainty of the evidence (i.e. risk of bias, inconsistency, indirectness or publication bias), then the interpretation in Sections 15.3.1 and 15.3.2 . about the relation of the confidence interval to the true effect may be carried forward to the overall certainty. However, if there are concerns about the other domains that affect the certainty of the evidence, the interpretation about the true effect needs to be seen in the context of further uncertainty resulting from those concerns.

For example, nine randomized controlled trials in almost 6000 cancer patients indicated that the administration of heparin reduces the risk of venous thromboembolism (VTE), with a risk ratio of 43% (95% CI 19% to 60%) (Akl et al 2011a). For patients with a plausible baseline risk of approximately 4.6% per year, this relative effect suggests that heparin leads to an absolute risk reduction of 20 fewer VTEs (95% CI 9 fewer to 27 fewer) per 1000 people per year (Akl et al 2011a). Now consider that the review authors or those applying the evidence in a guideline have lowered the certainty in the evidence as a result of indirectness. While the confidence intervals would remain unchanged, the certainty in that confidence interval and in the point estimate as reflecting the truth for the question of interest will be lowered. In fact, the certainty range will have unknown width so there will be unknown likelihood of a result within that range because of this indirectness. The lower the certainty in the evidence, the less we know about the width of the certainty range, although methods for quantifying risk of bias and understanding potential direction of bias may offer insight when lowered certainty is due to risk of bias. Nevertheless, decision makers must consider this uncertainty, and must do so in relation to the effect measure that is being evaluated (e.g. a relative or absolute measure). We will describe the impact on interpretations for dichotomous outcomes in Section 15.4 .

15.4 Interpreting results from dichotomous outcomes (including numbers needed to treat)

15.4.1 relative and absolute risk reductions.

Clinicians may be more inclined to prescribe an intervention that reduces the relative risk of death by 25% than one that reduces the risk of death by 1 percentage point, although both presentations of the evidence may relate to the same benefit (i.e. a reduction in risk from 4% to 3%). The former refers to the relative reduction in risk and the latter to the absolute reduction in risk. As described in Chapter 6, Section 6.4.1 , there are several measures for comparing dichotomous outcomes in two groups. Meta-analyses are usually undertaken using risk ratios (RR), odds ratios (OR) or risk differences (RD), but there are several alternative ways of expressing results.

Relative risk reduction (RRR) is a convenient way of re-expressing a risk ratio as a percentage reduction:

For example, a risk ratio of 0.75 translates to a relative risk reduction of 25%, as in the example above.

The risk difference is often referred to as the absolute risk reduction (ARR) or absolute risk increase (ARI), and may be presented as a percentage (e.g. 1%), as a decimal (e.g. 0.01), or as account (e.g. 10 out of 1000). We consider different choices for presenting absolute effects in Section 15.4.3 . We then describe computations for obtaining these numbers from the results of individual studies and of meta-analyses in Section 15.4.4 .

15.4.2 Number needed to treat (NNT)

The number needed to treat (NNT) is a common alternative way of presenting information on the effect of an intervention. The NNT is defined as the expected number of people who need to receive the experimental rather than the comparator intervention for one additional person to either incur or avoid an event (depending on the direction of the result) in a given time frame. Thus, for example, an NNT of 10 can be interpreted as ‘it is expected that one additional (or less) person will incur an event for every 10 participants receiving the experimental intervention rather than comparator over a given time frame’. It is important to be clear that:

• since the NNT is derived from the risk difference, it is still a comparative measure of effect (experimental versus a specific comparator) and not a general property of a single intervention; and
• the NNT gives an ‘expected value’. For example, NNT = 10 does not imply that one additional event will occur in each and every group of 10 people.

NNTs can be computed for both beneficial and detrimental events, and for interventions that cause both improvements and deteriorations in outcomes. In all instances NNTs are expressed as positive whole numbers. Some authors use the term ‘number needed to harm’ (NNH) when an intervention leads to an adverse outcome, or a decrease in a positive outcome, rather than improvement. However, this phrase can be misleading (most notably, it can easily be read to imply the number of people who will experience a harmful outcome if given the intervention), and it is strongly recommended that ‘number needed to harm’ and ‘NNH’ are avoided. The preferred alternative is to use phrases such as ‘number needed to treat for an additional beneficial outcome’ (NNTB) and ‘number needed to treat for an additional harmful outcome’ (NNTH) to indicate direction of effect.

As NNTs refer to events, their interpretation needs to be worded carefully when the binary outcome is a dichotomization of a scale-based outcome. For example, if the outcome is pain measured on a ‘none, mild, moderate or severe’ scale it may have been dichotomized as ‘none or mild’ versus ‘moderate or severe’. It would be inappropriate for an NNT from these data to be referred to as an ‘NNT for pain’. It is an ‘NNT for moderate or severe pain’.

We consider different choices for presenting absolute effects in Section 15.4.3 . We then describe computations for obtaining these numbers from the results of individual studies and of meta-analyses in Section 15.4.4 .

15.4.3 Expressing risk differences

Users of reviews are liable to be influenced by the choice of statistical presentations of the evidence. Hoffrage and colleagues suggest that physicians’ inferences about statistical outcomes are more appropriate when they deal with ‘natural frequencies’ – whole numbers of people, both treated and untreated (e.g. treatment results in a drop from 20 out of 1000 to 10 out of 1000 women having breast cancer) – than when effects are presented as percentages (e.g. 1% absolute reduction in breast cancer risk) (Hoffrage et al 2000). Probabilities may be more difficult to understand than frequencies, particularly when events are rare. While standardization may be important in improving the presentation of research evidence (and participation in healthcare decisions), current evidence suggests that the presentation of natural frequencies for expressing differences in absolute risk is best understood by consumers of healthcare information (Akl et al 2011b). This evidence provides the rationale for presenting absolute risks in ‘Summary of findings’ tables as numbers of people with events per 1000 people receiving the intervention (see Chapter 14 ).

RRs and RRRs remain crucial because relative effects tend to be substantially more stable across risk groups than absolute effects (see Chapter 10, Section 10.4.3 ). Review authors can use their own data to study this consistency (Cates 1999, Smeeth et al 1999). Risk differences from studies are least likely to be consistent across baseline event rates; thus, they are rarely appropriate for computing numbers needed to treat in systematic reviews. If a relative effect measure (OR or RR) is chosen for meta-analysis, then a comparator group risk needs to be specified as part of the calculation of an RD or NNT. In addition, if there are several different groups of participants with different levels of risk, it is crucial to express absolute benefit for each clinically identifiable risk group, clarifying the time period to which this applies. Studies in patients with differing severity of disease, or studies with different lengths of follow-up will almost certainly have different comparator group risks. In these cases, different comparator group risks lead to different RDs and NNTs (except when the intervention has no effect). A recommended approach is to re-express an odds ratio or a risk ratio as a variety of RD or NNTs across a range of assumed comparator risks (ACRs) (McQuay and Moore 1997, Smeeth et al 1999). Review authors should bear these considerations in mind not only when constructing their ‘Summary of findings’ table, but also in the text of their review.

For example, a review of oral anticoagulants to prevent stroke presented information to users by describing absolute benefits for various baseline risks (Aguilar and Hart 2005, Aguilar et al 2007). They presented their principal findings as “The inherent risk of stroke should be considered in the decision to use oral anticoagulants in atrial fibrillation patients, selecting those who stand to benefit most for this therapy” (Aguilar and Hart 2005). Among high-risk atrial fibrillation patients with prior stroke or transient ischaemic attack who have stroke rates of about 12% (120 per 1000) per year, warfarin prevents about 70 strokes yearly per 1000 patients, whereas for low-risk atrial fibrillation patients (with a stroke rate of about 2% per year or 20 per 1000), warfarin prevents only 12 strokes. This presentation helps users to understand the important impact that typical baseline risks have on the absolute benefit that they can expect.

15.4.4 Computations

Direct computation of risk difference (RD) or a number needed to treat (NNT) depends on the summary statistic (odds ratio, risk ratio or risk differences) available from the study or meta-analysis. When expressing results of meta-analyses, review authors should use, in the computations, whatever statistic they determined to be the most appropriate summary for meta-analysis (see Chapter 10, Section 10.4.3 ). Here we present calculations to obtain RD as a reduction in the number of participants per 1000. For example, a risk difference of –0.133 corresponds to 133 fewer participants with the event per 1000.

RDs and NNTs should not be computed from the aggregated total numbers of participants and events across the trials. This approach ignores the randomization within studies, and may produce seriously misleading results if there is unbalanced randomization in any of the studies. Using the pooled result of a meta-analysis is more appropriate. When computing NNTs, the values obtained are by convention always rounded up to the next whole number.

15.4.4.1 Computing NNT from a risk difference (RD)

A NNT may be computed from a risk difference as

where the vertical bars (‘absolute value of’) in the denominator indicate that any minus sign should be ignored. It is convention to round the NNT up to the nearest whole number. For example, if the risk difference is –0.12 the NNT is 9; if the risk difference is –0.22 the NNT is 5. Cochrane Review authors should qualify the NNT as referring to benefit (improvement) or harm by denoting the NNT as NNTB or NNTH. Note that this approach, although feasible, should be used only for the results of a meta-analysis of risk differences. In most cases meta-analyses will be undertaken using a relative measure of effect (RR or OR), and those statistics should be used to calculate the NNT (see Section 15.4.4.2 and 15.4.4.3 ).

15.4.4.2 Computing risk differences or NNT from a risk ratio

To aid interpretation of the results of a meta-analysis of risk ratios, review authors may compute an absolute risk reduction or NNT. In order to do this, an assumed comparator risk (ACR) (otherwise known as a baseline risk, or risk that the outcome of interest would occur with the comparator intervention) is required. It will usually be appropriate to do this for a range of different ACRs. The computation proceeds as follows:

As an example, suppose the risk ratio is RR = 0.92, and an ACR = 0.3 (300 per 1000) is assumed. Then the effect on risk is 24 fewer per 1000:

The NNT is 42:

15.4.4.3 Computing risk differences or NNT from an odds ratio

Review authors may wish to compute a risk difference or NNT from the results of a meta-analysis of odds ratios. In order to do this, an ACR is required. It will usually be appropriate to do this for a range of different ACRs. The computation proceeds as follows:

As an example, suppose the odds ratio is OR = 0.73, and a comparator risk of ACR = 0.3 is assumed. Then the effect on risk is 62 fewer per 1000:

The NNT is 17:

15.4.4.4 Computing risk ratio from an odds ratio

Because risk ratios are easier to interpret than odds ratios, but odds ratios have favourable mathematical properties, a review author may decide to undertake a meta-analysis based on odds ratios, but to express the result as a summary risk ratio (or relative risk reduction). This requires an ACR. Then

It will often be reasonable to perform this transformation using the median comparator group risk from the studies in the meta-analysis.

15.4.4.5 Computing confidence limits

Confidence limits for RDs and NNTs may be calculated by applying the above formulae to the upper and lower confidence limits for the summary statistic (RD, RR or OR) (Altman 1998). Note that this confidence interval does not incorporate uncertainty around the ACR.

If the 95% confidence interval of OR or RR includes the value 1, one of the confidence limits will indicate benefit and the other harm. Thus, appropriate use of the words ‘fewer’ and ‘more’ is required for each limit when presenting results in terms of events. For NNTs, the two confidence limits should be labelled as NNTB and NNTH to indicate the direction of effect in each case. The confidence interval for the NNT will include a ‘discontinuity’, because increasingly smaller risk differences that approach zero will lead to NNTs approaching infinity. Thus, the confidence interval will include both an infinitely large NNTB and an infinitely large NNTH.

15.5 Interpreting results from continuous outcomes (including standardized mean differences)

15.5.1 meta-analyses with continuous outcomes.

Review authors should describe in the study protocol how they plan to interpret results for continuous outcomes. When outcomes are continuous, review authors have a number of options to present summary results. These options differ if studies report the same measure that is familiar to the target audiences, studies report the same or very similar measures that are less familiar to the target audiences, or studies report different measures.

15.5.2 Meta-analyses with continuous outcomes using the same measure

If all studies have used the same familiar units, for instance, results are expressed as durations of events, such as symptoms for conditions including diarrhoea, sore throat, otitis media, influenza or duration of hospitalization, a meta-analysis may generate a summary estimate in those units, as a difference in mean response (see, for instance, the row summarizing results for duration of diarrhoea in Chapter 14, Figure 14.1.b and the row summarizing oedema in Chapter 14, Figure 14.1.a ). For such outcomes, the ‘Summary of findings’ table should include a difference of means between the two interventions. However, when units of such outcomes may be difficult to interpret, particularly when they relate to rating scales (again, see the oedema row of Chapter 14, Figure 14.1.a ). ‘Summary of findings’ tables should include the minimum and maximum of the scale of measurement, and the direction. Knowledge of the smallest change in instrument score that patients perceive is important – the minimal important difference (MID) – and can greatly facilitate the interpretation of results (Guyatt et al 1998, Schünemann and Guyatt 2005). Knowing the MID allows review authors and users to place results in context. Review authors should state the MID – if known – in the Comments column of their ‘Summary of findings’ table. For example, the chronic respiratory questionnaire has possible scores in health-related quality of life ranging from 1 to 7 and 0.5 represents a well-established MID (Jaeschke et al 1989, Schünemann et al 2005).

15.5.3 Meta-analyses with continuous outcomes using different measures

When studies have used different instruments to measure the same construct, a standardized mean difference (SMD) may be used in meta-analysis for combining continuous data. Without guidance, clinicians and patients may have little idea how to interpret results presented as SMDs. Review authors should therefore consider issues of interpretability when planning their analysis at the protocol stage and should consider whether there will be suitable ways to re-express the SMD or whether alternative effect measures, such as a ratio of means, or possibly as minimal important difference units (Guyatt et al 2013b) should be used. Table 15.5.a and the following sections describe these options.

Table 15.5.a Approaches and their implications to presenting results of continuous variables when primary studies have used different instruments to measure the same construct. Adapted from Guyatt et al (2013b)

15.5.3.1 Presenting and interpreting SMDs using generic effect size estimates

The SMD expresses the intervention effect in standard units rather than the original units of measurement. The SMD is the difference in mean effects between the experimental and comparator groups divided by the pooled standard deviation of participants’ outcomes, or external SDs when studies are very small (see Chapter 6, Section 6.5.1.2 ). The value of a SMD thus depends on both the size of the effect (the difference between means) and the standard deviation of the outcomes (the inherent variability among participants or based on an external SD).

If review authors use the SMD, they might choose to present the results directly as SMDs (row 1a, Table 15.5.a and Table 15.5.b ). However, absolute values of the intervention and comparison groups are typically not useful because studies have used different measurement instruments with different units. Guiding rules for interpreting SMDs (or ‘Cohen’s effect sizes’) exist, and have arisen mainly from researchers in the social sciences (Cohen 1988). One example is as follows: 0.2 represents a small effect, 0.5 a moderate effect and 0.8 a large effect (Cohen 1988). Variations exist (e.g. <0.40=small, 0.40 to 0.70=moderate, >0.70=large). Review authors might consider including such a guiding rule in interpreting the SMD in the text of the review, and in summary versions such as the Comments column of a ‘Summary of findings’ table. However, some methodologists believe that such interpretations are problematic because patient importance of a finding is context-dependent and not amenable to generic statements.

15.5.3.2 Re-expressing SMDs using a familiar instrument

The second possibility for interpreting the SMD is to express it in the units of one or more of the specific measurement instruments used by the included studies (row 1b, Table 15.5.a and Table 15.5.b ). The approach is to calculate an absolute difference in means by multiplying the SMD by an estimate of the SD associated with the most familiar instrument. To obtain this SD, a reasonable option is to calculate a weighted average across all intervention groups of all studies that used the selected instrument (preferably a pre-intervention or post-intervention SD as discussed in Chapter 10, Section 10.5.2 ). To better reflect among-person variation in practice, or to use an instrument not represented in the meta-analysis, it may be preferable to use a standard deviation from a representative observational study. The summary effect is thus re-expressed in the original units of that particular instrument and the clinical relevance and impact of the intervention effect can be interpreted using that familiar instrument.

The same approach of re-expressing the results for a familiar instrument can also be used for other standardized effect measures such as when standardizing by MIDs (Guyatt et al 2013b): see Section 15.5.3.5 .

Table 15.5.b Application of approaches when studies have used different measures: effects of dexamethasone for pain after laparoscopic cholecystectomy (Karanicolas et al 2008). Reproduced with permission of Wolters Kluwer

1 Certainty rated according to GRADE from very low to high certainty. 2 Substantial unexplained heterogeneity in study results. 3 Imprecision due to wide confidence intervals. 4 The 20% comes from the proportion in the control group requiring rescue analgesia. 5 Crude (arithmetic) means of the post-operative pain mean responses across all five trials when transformed to a 100-point scale.

15.5.3.3 Re-expressing SMDs through dichotomization and transformation to relative and absolute measures

A third approach (row 1c, Table 15.5.a and Table 15.5.b ) relies on converting the continuous measure into a dichotomy and thus allows calculation of relative and absolute effects on a binary scale. A transformation of a SMD to a (log) odds ratio is available, based on the assumption that an underlying continuous variable has a logistic distribution with equal standard deviation in the two intervention groups, as discussed in Chapter 10, Section 10.6  (Furukawa 1999, Guyatt et al 2013b). The assumption is unlikely to hold exactly and the results must be regarded as an approximation. The log odds ratio is estimated as

(or approximately 1.81✕SMD). The resulting odds ratio can then be presented as normal, and in a ‘Summary of findings’ table, combined with an assumed comparator group risk to be expressed as an absolute risk difference. The comparator group risk in this case would refer to the proportion of people who have achieved a specific value of the continuous outcome. In randomized trials this can be interpreted as the proportion who have improved by some (specified) amount (responders), for instance by 5 points on a 0 to 100 scale. Table 15.5.c shows some illustrative results from this method. The risk differences can then be converted to NNTs or to people per thousand using methods described in Section 15.4.4 .

Table 15.5.c Risk difference derived for specific SMDs for various given ‘proportions improved’ in the comparator group (Furukawa 1999, Guyatt et al 2013b). Reproduced with permission of Elsevier 

15.5.3.4 Ratio of means

A more frequently used approach is based on calculation of a ratio of means between the intervention and comparator groups (Friedrich et al 2008) as discussed in Chapter 6, Section 6.5.1.3 . Interpretational advantages of this approach include the ability to pool studies with outcomes expressed in different units directly, to avoid the vulnerability of heterogeneous populations that limits approaches that rely on SD units, and for ease of clinical interpretation (row 2, Table 15.5.a and Table 15.5.b ). This method is currently designed for post-intervention scores only. However, it is possible to calculate a ratio of change scores if both intervention and comparator groups change in the same direction in each relevant study, and this ratio may sometimes be informative.

Limitations to this approach include its limited applicability to change scores (since it is unlikely that both intervention and comparator group changes are in the same direction in all studies) and the possibility of misleading results if the comparator group mean is very small, in which case even a modest difference from the intervention group will yield a large and therefore misleading ratio of means. It also requires that separate ratios of means be calculated for each included study, and then entered into a generic inverse variance meta-analysis (see Chapter 10, Section 10.3 ).

The ratio of means approach illustrated in Table 15.5.b suggests a relative reduction in pain of only 13%, meaning that those receiving steroids have a pain severity 87% of those in the comparator group, an effect that might be considered modest.

15.5.3.5 Presenting continuous results as minimally important difference units

To express results in MID units, review authors have two options. First, they can be combined across studies in the same way as the SMD, but instead of dividing the mean difference of each study by its SD, review authors divide by the MID associated with that outcome (Johnston et al 2010, Guyatt et al 2013b). Instead of SD units, the pooled results represent MID units (row 3, Table 15.5.a and Table 15.5.b ), and may be more easily interpretable. This approach avoids the problem of varying SDs across studies that may distort estimates of effect in approaches that rely on the SMD. The approach, however, relies on having well-established MIDs. The approach is also risky in that a difference less than the MID may be interpreted as trivial when a substantial proportion of patients may have achieved an important benefit.

The other approach makes a simple conversion (not shown in Table 15.5.b ), before undertaking the meta-analysis, of the means and SDs from each study to means and SDs on the scale of a particular familiar instrument whose MID is known. For example, one can rescale the mean and SD of other chronic respiratory disease instruments (e.g. rescaling a 0 to 100 score of an instrument) to a the 1 to 7 score in Chronic Respiratory Disease Questionnaire (CRQ) units (by assuming 0 equals 1 and 100 equals 7 on the CRQ). Given the MID of the CRQ of 0.5, a mean difference in change of 0.71 after rescaling of all studies suggests a substantial effect of the intervention (Guyatt et al 2013b). This approach, presenting in units of the most familiar instrument, may be the most desirable when the target audiences have extensive experience with that instrument, particularly if the MID is well established.

15.6 Drawing conclusions

15.6.1 conclusions sections of a cochrane review.

Authors’ conclusions in a Cochrane Review are divided into implications for practice and implications for research. While Cochrane Reviews about interventions can provide meaningful information and guidance for practice, decisions about the desirable and undesirable consequences of healthcare options require evidence and judgements for criteria that most Cochrane Reviews do not provide (Alonso-Coello et al 2016). In describing the implications for practice and the development of recommendations, however, review authors may consider the certainty of the evidence, the balance of benefits and harms, and assumed values and preferences.

15.6.2 Implications for practice

Drawing conclusions about the practical usefulness of an intervention entails making trade-offs, either implicitly or explicitly, between the estimated benefits, harms and the values and preferences. Making such trade-offs, and thus making specific recommendations for an action in a specific context, goes beyond a Cochrane Review and requires additional evidence and informed judgements that most Cochrane Reviews do not provide (Alonso-Coello et al 2016). Such judgements are typically the domain of clinical practice guideline developers for which Cochrane Reviews will provide crucial information (Graham et al 2011, Schünemann et al 2014, Zhang et al 2018a). Thus, authors of Cochrane Reviews should not make recommendations.

If review authors feel compelled to lay out actions that clinicians and patients could take, they should – after describing the certainty of evidence and the balance of benefits and harms – highlight different actions that might be consistent with particular patterns of values and preferences. Other factors that might influence a decision should also be highlighted, including any known factors that would be expected to modify the effects of the intervention, the baseline risk or status of the patient, costs and who bears those costs, and the availability of resources. Review authors should ensure they consider all patient-important outcomes, including those for which limited data may be available. In the context of public health reviews the focus may be on population-important outcomes as the target may be an entire (non-diseased) population and include outcomes that are not measured in the population receiving an intervention (e.g. a reduction of transmission of infections from those receiving an intervention). This process implies a high level of explicitness in judgements about values or preferences attached to different outcomes and the certainty of the related evidence (Zhang et al 2018b, Zhang et al 2018c); this and a full cost-effectiveness analysis is beyond the scope of most Cochrane Reviews (although they might well be used for such analyses; see Chapter 20 ).

A review on the use of anticoagulation in cancer patients to increase survival (Akl et al 2011a) provides an example for laying out clinical implications for situations where there are important trade-offs between desirable and undesirable effects of the intervention: “The decision for a patient with cancer to start heparin therapy for survival benefit should balance the benefits and downsides and integrate the patient’s values and preferences. Patients with a high preference for a potential survival prolongation, limited aversion to potential bleeding, and who do not consider heparin (both UFH or LMWH) therapy a burden may opt to use heparin, while those with aversion to bleeding may not.”

15.6.3 Implications for research

The second category for authors’ conclusions in a Cochrane Review is implications for research. To help people make well-informed decisions about future healthcare research, the ‘Implications for research’ section should comment on the need for further research, and the nature of the further research that would be most desirable. It is helpful to consider the population, intervention, comparison and outcomes that could be addressed, or addressed more effectively in the future, in the context of the certainty of the evidence in the current review (Brown et al 2006):

• P (Population): diagnosis, disease stage, comorbidity, risk factor, sex, age, ethnic group, specific inclusion or exclusion criteria, clinical setting;
• I (Intervention): type, frequency, dose, duration, prognostic factor;
• C (Comparison): placebo, routine care, alternative treatment/management;
• O (Outcome): which clinical or patient-related outcomes will the researcher need to measure, improve, influence or accomplish? Which methods of measurement should be used?

While Cochrane Review authors will find the PICO domains helpful, the domains of the GRADE certainty framework further support understanding and describing what additional research will improve the certainty in the available evidence. Note that as the certainty of the evidence is likely to vary by outcome, these implications will be specific to certain outcomes in the review. Table 15.6.a shows how review authors may be aided in their interpretation of the body of evidence and drawing conclusions about future research and practice.

Table 15.6.a Implications for research and practice suggested by individual GRADE domains

The review of compression stockings for prevention of deep vein thrombosis (DVT) in airline passengers described in Chapter 14 provides an example where there is some convincing evidence of a benefit of the intervention: “This review shows that the question of the effects on symptomless DVT of wearing versus not wearing compression stockings in the types of people studied in these trials should now be regarded as answered. Further research may be justified to investigate the relative effects of different strengths of stockings or of stockings compared to other preventative strategies. Further randomised trials to address the remaining uncertainty about the effects of wearing versus not wearing compression stockings on outcomes such as death, pulmonary embolism and symptomatic DVT would need to be large.” (Clarke et al 2016).

A review of therapeutic touch for anxiety disorder provides an example of the implications for research when no eligible studies had been found: “This review highlights the need for randomized controlled trials to evaluate the effectiveness of therapeutic touch in reducing anxiety symptoms in people diagnosed with anxiety disorders. Future trials need to be rigorous in design and delivery, with subsequent reporting to include high quality descriptions of all aspects of methodology to enable appraisal and interpretation of results.” (Robinson et al 2007).

15.6.4 Reaching conclusions

A common mistake is to confuse ‘no evidence of an effect’ with ‘evidence of no effect’. When the confidence intervals are too wide (e.g. including no effect), it is wrong to claim that the experimental intervention has ‘no effect’ or is ‘no different’ from the comparator intervention. Review authors may also incorrectly ‘positively’ frame results for some effects but not others. For example, when the effect estimate is positive for a beneficial outcome but confidence intervals are wide, review authors may describe the effect as promising. However, when the effect estimate is negative for an outcome that is considered harmful but the confidence intervals include no effect, review authors report no effect. Another mistake is to frame the conclusion in wishful terms. For example, review authors might write, “there were too few people in the analysis to detect a reduction in mortality” when the included studies showed a reduction or even increase in mortality that was not ‘statistically significant’. One way of avoiding errors such as these is to consider the results blinded; that is, consider how the results would be presented and framed in the conclusions if the direction of the results was reversed. If the confidence interval for the estimate of the difference in the effects of the interventions overlaps with no effect, the analysis is compatible with both a true beneficial effect and a true harmful effect. If one of the possibilities is mentioned in the conclusion, the other possibility should be mentioned as well. Table 15.6.b suggests narrative statements for drawing conclusions based on the effect estimate from the meta-analysis and the certainty of the evidence.

Table 15.6.b Suggested narrative statements for phrasing conclusions

Another common mistake is to reach conclusions that go beyond the evidence. Often this is done implicitly, without referring to the additional information or judgements that are used in reaching conclusions about the implications of a review for practice. Even when additional information and explicit judgements support conclusions about the implications of a review for practice, review authors rarely conduct systematic reviews of the additional information. Furthermore, implications for practice are often dependent on specific circumstances and values that must be taken into consideration. As we have noted, review authors should always be cautious when drawing conclusions about implications for practice and they should not make recommendations.

15.7 Chapter information

Authors: Holger J Schünemann, Gunn E Vist, Julian PT Higgins, Nancy Santesso, Jonathan J Deeks, Paul Glasziou, Elie Akl, Gordon H Guyatt; on behalf of the Cochrane GRADEing Methods Group

Acknowledgements: Andrew Oxman, Jonathan Sterne, Michael Borenstein and Rob Scholten contributed text to earlier versions of this chapter.

Funding: This work was in part supported by funding from the Michael G DeGroote Cochrane Canada Centre and the Ontario Ministry of Health. JJD receives support from the National Institute for Health Research (NIHR) Birmingham Biomedical Research Centre at the University Hospitals Birmingham NHS Foundation Trust and the University of Birmingham. JPTH receives support from the NIHR Biomedical Research Centre at University Hospitals Bristol NHS Foundation Trust and the University of Bristol. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

15.8 References

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Guyatt G, Oxman AD, Akl EA, Kunz R, Vist G, Brozek J, Norris S, Falck-Ytter Y, Glasziou P, DeBeer H, Jaeschke R, Rind D, Meerpohl J, Dahm P, Schünemann HJ. GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables. Journal of Clinical Epidemiology 2011a; 64 : 383-394.

Guyatt GH, Juniper EF, Walter SD, Griffith LE, Goldstein RS. Interpreting treatment effects in randomised trials. BMJ 1998; 316 : 690-693.

Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, Schünemann HJ. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ 2008; 336 : 924-926.

Guyatt GH, Oxman AD, Kunz R, Woodcock J, Brozek J, Helfand M, Alonso-Coello P, Falck-Ytter Y, Jaeschke R, Vist G, Akl EA, Post PN, Norris S, Meerpohl J, Shukla VK, Nasser M, Schünemann HJ. GRADE guidelines: 8. Rating the quality of evidence--indirectness. Journal of Clinical Epidemiology 2011b; 64 : 1303-1310.

Guyatt GH, Oxman AD, Santesso N, Helfand M, Vist G, Kunz R, Brozek J, Norris S, Meerpohl J, Djulbegovic B, Alonso-Coello P, Post PN, Busse JW, Glasziou P, Christensen R, Schünemann HJ. GRADE guidelines: 12. Preparing summary of findings tables-binary outcomes. Journal of Clinical Epidemiology 2013a; 66 : 158-172.

Guyatt GH, Thorlund K, Oxman AD, Walter SD, Patrick D, Furukawa TA, Johnston BC, Karanicolas P, Akl EA, Vist G, Kunz R, Brozek J, Kupper LL, Martin SL, Meerpohl JJ, Alonso-Coello P, Christensen R, Schünemann HJ. GRADE guidelines: 13. Preparing summary of findings tables and evidence profiles-continuous outcomes. Journal of Clinical Epidemiology 2013b; 66 : 173-183.

Hawe P, Shiell A, Riley T, Gold L. Methods for exploring implementation variation and local context within a cluster randomised community intervention trial. Journal of Epidemiology and Community Health 2004; 58 : 788-793.

Hoffrage U, Lindsey S, Hertwig R, Gigerenzer G. Medicine. Communicating statistical information. Science 2000; 290 : 2261-2262.

Jaeschke R, Singer J, Guyatt GH. Measurement of health status. Ascertaining the minimal clinically important difference. Controlled Clinical Trials 1989; 10 : 407-415.

Johnston B, Thorlund K, Schünemann H, Xie F, Murad M, Montori V, Guyatt G. Improving the interpretation of health-related quality of life evidence in meta-analysis: The application of minimal important difference units. . Health Outcomes and Qualithy of Life 2010; 11 : 116.

Karanicolas PJ, Smith SE, Kanbur B, Davies E, Guyatt GH. The impact of prophylactic dexamethasone on nausea and vomiting after laparoscopic cholecystectomy: a systematic review and meta-analysis. Annals of Surgery 2008; 248 : 751-762.

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McQuay HJ, Moore RA. Using numerical results from systematic reviews in clinical practice. Annals of Internal Medicine 1997; 126 : 712-720.

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Robinson J, Biley FC, Dolk H. Therapeutic touch for anxiety disorders. Cochrane Database of Systematic Reviews 2007; 3 : CD006240.

Rothwell PM. External validity of randomised controlled trials: "to whom do the results of this trial apply?". Lancet 2005; 365 : 82-93.

Santesso N, Carrasco-Labra A, Langendam M, Brignardello-Petersen R, Mustafa RA, Heus P, Lasserson T, Opiyo N, Kunnamo I, Sinclair D, Garner P, Treweek S, Tovey D, Akl EA, Tugwell P, Brozek JL, Guyatt G, Schünemann HJ. Improving GRADE evidence tables part 3: detailed guidance for explanatory footnotes supports creating and understanding GRADE certainty in the evidence judgments. Journal of Clinical Epidemiology 2016; 74 : 28-39.

Schünemann HJ, Puhan M, Goldstein R, Jaeschke R, Guyatt GH. Measurement properties and interpretability of the Chronic respiratory disease questionnaire (CRQ). COPD: Journal of Chronic Obstructive Pulmonary Disease 2005; 2 : 81-89.

Schünemann HJ, Guyatt GH. Commentary--goodbye M(C)ID! Hello MID, where do you come from? Health Services Research 2005; 40 : 593-597.

Schünemann HJ, Fretheim A, Oxman AD. Improving the use of research evidence in guideline development: 13. Applicability, transferability and adaptation. Health Research Policy and Systems 2006; 4 : 25.

Schünemann HJ. Methodological idiosyncracies, frameworks and challenges of non-pharmaceutical and non-technical treatment interventions. Zeitschrift für Evidenz, Fortbildung und Qualität im Gesundheitswesen 2013; 107 : 214-220.

Schünemann HJ, Tugwell P, Reeves BC, Akl EA, Santesso N, Spencer FA, Shea B, Wells G, Helfand M. Non-randomized studies as a source of complementary, sequential or replacement evidence for randomized controlled trials in systematic reviews on the effects of interventions. Research Synthesis Methods 2013; 4 : 49-62.

Schünemann HJ, Wiercioch W, Etxeandia I, Falavigna M, Santesso N, Mustafa R, Ventresca M, Brignardello-Petersen R, Laisaar KT, Kowalski S, Baldeh T, Zhang Y, Raid U, Neumann I, Norris SL, Thornton J, Harbour R, Treweek S, Guyatt G, Alonso-Coello P, Reinap M, Brozek J, Oxman A, Akl EA. Guidelines 2.0: systematic development of a comprehensive checklist for a successful guideline enterprise. CMAJ: Canadian Medical Association Journal 2014; 186 : E123-142.

Schünemann HJ. Interpreting GRADE's levels of certainty or quality of the evidence: GRADE for statisticians, considering review information size or less emphasis on imprecision? Journal of Clinical Epidemiology 2016; 75 : 6-15.

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Sun X, Briel M, Busse JW, You JJ, Akl EA, Mejza F, Bala MM, Bassler D, Mertz D, Diaz-Granados N, Vandvik PO, Malaga G, Srinathan SK, Dahm P, Johnston BC, Alonso-Coello P, Hassouneh B, Walter SD, Heels-Ansdell D, Bhatnagar N, Altman DG, Guyatt GH. Credibility of claims of subgroup effects in randomised controlled trials: systematic review. BMJ 2012; 344 : e1553.

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Making Inferences and Drawing Conclusions

LESSON Sometimes authors A person who wrote a text. leave out information, which means the reader has to think to figure out what he or she is trying to say. This is known as the subtext Information that is left out of a reading. The author may do this because the audience knows this information, it may be unimportant, or because they want readers to figure out the information on their own by drawing inferences and conclusions. of a reading A piece of writing to be read. A reading can either be a full work (i.e., a book) or partial (i.e., a passage). . Writers may leave out information because they think the readers already know it, it may not seem important, or because they want the readers to find the meaning on their own. A reader who thinks about the subtext in a reading may make inferences Meaning that has been uncovered by considering the subtext of a reading, drawing from your own experiences, and connecting them to the reading. about what is happening based on the facts and details provided and may then draw conclusions Uncovering the subtext of a reading by thinking about the details the author provides and then considering what may happen as a result of those details. about what will happen as a result. When readers make inferences, they can often pull more information out from the story, making it more meaningful to them.

You can try various strategies to make inferences and draw conclusions about what you read. Here are three:

• Observe the details provided by the author.
• Draw from your experiences and connect them to the reading.
• Ask yourself what may happen as a result of what is taking place in the reading.

From there, you can use this formula to draw a conclusion:

Details from the reading + Your experiences = A conclusion about what is happening or will happen

People make inferences and draw conclusions about things they see, hear, and read in everyday life. For example, if you are at the store and see an elderly person staring at an item that is high on a shelf, you may infer that this person wants that item. As a result, you may offer to get it for him or her. Learning how to make inferences and draw conclusions can help you become a good observer of the world around you and become a better communicator.

Read the following passages A short portion of a writing taken from a larger source, such as a book, article, speech, or poem. and see examples of how to identify subtext in readings by making inferences and drawing conclusions.

1. After making a speech about the importance of studying, the instructor started passing the graded exams back to the class. Pausing by Jack's desk, he said, "Jack, I'd like to speak with you for a moment after class." Hearing this, Jack put his head down on his desk.

What are the significant details from the reading?

• Instructor talking about studying
• Instructor passing back graded exams
• Instructor asking to speak with Jack after class
• Jack putting his head down on his desk

What are some of your own experiences related to the reading?

When an instructor wants to speak with you after class, it's usually bad.

What conclusion could be made?

Jack probably did poorly on his exam.

2. Paula was excited to get to campus and sign up for classes. After last semester, she knew she could do anything she put her mind to.

• Paula was signing up for classes
• She was excited
• She was a student last semester

Students become more confident and excited when they have had good experiences with their instructors and classes.

Paula probably had at least one very good instructor, worked hard last term, and earned good grades. Her success has given her enough confidence that she now believes that she can finish her college career successfully.

Identify subtext in the readings by making inferences and drawing conclusions based on the following passages.

• Jan sighed as she pulled out her credit card again to pay for the groceries that were already bagged. She didn't want to use her credit card, but she needed groceries for the week. She decided then and there to attend a meeting on how to manage her money.

Sample Answer

• Jan sighed, showing that she is upset.
• She has reluctantly used a credit card to pay for her groceries on more than one occasion.
• She plans on attending a money management meeting.

I try not to use my credit card very much, but sometimes I don't think I can avoid it because I need to pay for school, rent, and groceries. I'm scared, though, that I will build up so much debt that I will not be able to pay it off.

Jan seems to be spending more than she makes, so she is relying on her credit card. However, she wants to gain control over her spending, so she wants help.

• The guest speaker gave several examples of how to live within a budget. She also spoke about a variety of aid and grant programs available to students. Jan wrote down all of these programs in her notes.
• There was a guest speaker who spoke about how to live within a budget.
• Jan wrote down the names of several aid and grant programs mentioned by the speaker.

I have been helped by programs like financial aid to pay for school and it made it easier for me to pay my bills.

Jan attended a meeting about budgeting and was learning how to manage her money better by seeking out assistance.

• Jan felt proud of herself for attending the money management seminar – so much so that she decided to treat herself by going to the shopping mall.
• When the meeting finished, Jan went to the mall.

Jan reminds me of my aunt. Every year she gets a big tax refund and instead of paying down debt or putting any money away in savings, she goes out and buys an expensive item like a big-screen TV or a DVD system for her minivan.

Jan didn't learn much from the seminar. Even though she looked for help, she will still need to pay attention to what she buys and try not to spend so much money.

When I read an essay, an article, or even a textbook, I try to make connections to my life. I like to see how the information relates to me and if I've had experiences similar to it. If I have, I find that I can relate to the information and that helps me learn and remember it.

Knowing about inferences helps me think about what my audience already knows. If I can decide what they may already know, I may not need to provide as much detail and that will help my writing sound less repetitive and obvious to the audience.

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2.1F: Analyzing Data and Drawing Conclusions

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Data analysis in sociological research aims to identify meaningful sociological patterns.

Learning Objectives

• Compare and contrast the analysis of quantitative vs. qualitative data
• Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making. Data analysis is a process, within which several phases can be distinguished.
• One way in which analysis can vary is by the nature of the data. Quantitative data is often analyzed using regressions. Regression analyses measure relationships between dependent and independent variables, taking the existence of unknown parameters into account.
• Qualitative data can be coded–that is, key concepts and variables are assigned a shorthand, and the data gathered are broken down into those concepts or variables. Coding allows sociologists to perform a more rigorous scientific analysis of the data.

Sociological data analysis is designed to produce patterns. It is important to remember, however, that correlation does not imply causation; in other words, just because variables change at a proportional rate, it does not follow that one variable influences the other.

• Without a valid design, valid scientific conclusions cannot be drawn. Internal validity concerns the degree to which conclusions about causality can be made. External validity concerns the extent to which the results of a study are generalizable.
• correlation : A reciprocal, parallel or complementary relationship between two or more comparable objects.
• causation : The act of causing; also the act or agency by which an effect is produced.
• Regression analysis : In statistics, regression analysis includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed.

The Process of Data Analysis

Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making. In statistical applications, some people divide data analysis into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data and CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics focuses on the application of statistical or structural models for predictive forecasting or classification. Text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data.

Data analysis is a process, within which several phases can be distinguished. The initial data analysis phase is guided by examining, among other things, the quality of the data (for example, the presence of missing or extreme observations), the quality of measurements, and if the implementation of the study was in line with the research design. In the main analysis phase, either an exploratory or confirmatory approach can be adopted. Usually the approach is decided before data is collected. In an exploratory analysis, no clear hypothesis is stated before analyzing the data, and the data is searched for models that describe the data well. In a confirmatory analysis, clear hypotheses about the data are tested.

Regression Analysis

The type of data analysis employed can vary. One way in which analysis often varies is by the quantitative or qualitative nature of the data.

Quantitative data can be analyzed in a variety of ways, regression analysis being among the most popular. Regression analyses measure relationships between dependent and independent variables, taking the existence of unknown parameters into account. More specifically, regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed.

A large body of techniques for carrying out regression analysis has been developed. In practice, the performance of regression analysis methods depends on the form of the data generating process and how it relates to the regression approach being used. Since the true form of the data-generating process is generally not known, regression analysis often depends to some extent on making assumptions about this process. These assumptions are sometimes testable if a large amount of data is available. Regression models for prediction are often useful even when the assumptions are moderately violated, although they may not perform optimally. However, in many applications, especially with small effects or questions of causality based on observational data, regression methods give misleading results.

Qualitative data can involve coding–that is, key concepts and variables are assigned a shorthand, and the data gathered is broken down into those concepts or variables. Coding allows sociologists to perform a more rigorous scientific analysis of the data. Coding is the process of categorizing qualitative data so that the data becomes quantifiable and thus measurable. Of course, before researchers can code raw data such as taped interviews, they need to have a clear research question. How data is coded depends entirely on what the researcher hopes to discover in the data; the same qualitative data can be coded in many different ways, calling attention to different aspects of the data.

Sociological Data Analysis

Correlation, Causation, and Spurious Relationships : This mock newscast gives three competing interpretations of the same survey findings and demonstrates the dangers of assuming that correlation implies causation.

Conclusions

In terms of the kinds of conclusions that can be drawn, a study and its results can be assessed in multiple ways. Without a valid design, valid scientific conclusions cannot be drawn. Internal validity is an inductive estimate of the degree to which conclusions about causal relationships can be made (e.g., cause and effect), based on the measures used, the research setting, and the whole research design. External validity concerns the extent to which the (internally valid) results of a study can be held to be true for other cases, such as to different people, places, or times. In other words, it is about whether findings can be validly generalized. Learning about and applying statistics (as well as knowing their limitations) can help you better understand sociological research and studies. Knowledge of statistics helps you makes sense of the numbers in terms of relationships, and it allows you to ask relevant questions about sociological phenomena.

EnglishTutorHub

Drawing/Making Conclusion Based on Information Given

In this session of tutorial for beginners, learners will develop their critical thinking skills from “Drawing/Making Conclusion Based on Information Given”. How to “Drawing/Making Conclusion Based on Information Given”?. How was conclusion are able to predict?. Why we nee to give the best conclusion after reading or experiencing the situation?.

This lesson assists students in going through the process of how to draw conclusion based on the information given.

I. Objectives: after this lesson the students will:

            1. Draw/make conclusion based on the information given

            2. Explain how a conclusion was reached

            3. Choose the best conclusion after reading the situation

II. Subject Matter: Drawing/Making Conclusion Based on Information Given

            Reading Selection: The Old Man and his Son

            Materials: 1. Copy of the Selection “The Old Man and His Son”

                             2. Exercises in Drawing/Making Conclusions

III. Procedure/ Activities

A. Pre- Activity

Read and Identify / infer the general mood or emotion of the character in the sentence.

  1. Eric ran after Danny and hit him. “Take that!” he said

            Why did you write on my book?”

 2. Mrs.   Santos won first prize in a raffle. She shared her money with he relatives. There was much rejoicing in their homes.

3.”Wow” It’s a big, big   three    layer cake! “Said the birthday girl when  the big box is open suddenly.

A.2 Word Drill: Flashcards

 Old man                      father and son                        awkward        

Curiosity                     passengers                               station

Couple                         passing air                               treatment

B. Activity Proper

1. Introduction

            Have you experienced riding a train with your father?

            What is your feeling when you’re inside the train?

2. Vocabulary building

Match the words from A to B

            A                                 B

1. curiosity                  a. medical care or attention

2. awkward                 b. traveler in or on a vehicle

3. treatment                 c. eager desire to know

4. passenger                 d. clumsy, embarrassed

5. couple                      e. two people who are married

3. Recall standards for silent reading

4. Reading of the Selection

            “The Old Man and His Son”

5. Comprehension Check:

1. Who is the old man?

2. Based on the information in the selection, what does treatment probably mean?

3. Why was the couple felt embarrassed with the 25 year old man?

4. Where did the story happen?

6. Discussion

Explain that drawing conclusions involves using background knowledge, information from the text, and personal experience to make inferences about characters, plot and theme of the text.

7. Group Activity

            a. Giving of Standard in group work

            b. Doing the Activity

            c. Publishing of Reports         

8. Analysis and Generalization

            1. In answering your group activity, what did you do?

            2. What were the bases of your conclusion?

            3. Therefore, how are you going to make or draw conclusion?

            Generalization:

Drawing/ making conclusion are based on the story selection read, situation, experiences and information given.

9. Fixation of Skill

Read the selection and then choose the statement that is most likely true.  1. Emir rarely rides to school each morning. He could take the school bus but prefers to walk the two miles from his home to school. He believes that the walk wakes him up and improves his learning throughout the day. a. Emir always rides the bus home at the end of the school day. b. Emir never rides the bus to school. c. Emir values learning. d. Emir is usually late for school. 2. “Remember, Maria,” Mrs. Russo said, “You’re very allergic to chocolate. Please don’t eat any chocolate candy at the Halloween party!” Three hours later, Maria was home from the party with a terrible stomach ache. “I promise you, Mom,” Maria groaned, “I’ll always do what you say from now on.” a. Maria got into a fight at the party. b. Maria ate chocolate at the party. c. Maria ate no chocolate at the party. d. Maria embarrassed herself by singing at the party.

IV. Evaluation/Assessment

Read the selection and then choose the statement that is most likely true.

1. In another week, we’ll be turning our clocks back one hour. Then it will be almost dark by the time most of us get home from school. It will seem as if we suddenly have less daylight each day. Of course, people aren’t actually changing the behavior of the sun or the earth by messing with their clocks. a. Our planet has to spin faster when we reset our clocks. b. It’s clear that turning our clocks back will help prevent global warming. c. If according to our clocks, the sun sets an hour earlier, it will also rise an hour earlier. d. The earth will actually rotate at a slower rate when we turn back the clocks.

2. Every time I give my cat a plate of tuna, he ignores it. Every time I give him a plate of salmon, he quickly eats it all. When I feed him beef, he eats a little bit of it, but doesn’t seem to love it. a. Cats prefer salmon over tuna. b. I give my cat more than one kind of food. c. My cat likes tuna better than beef. d. My cat won’t eat fish.

3. One of the oldest games in the world is hockey. The ancient Greeks and Persians played it. So did Native Americans. The name comes from an old French world, hoquet . It is the word for a shepherd’s crooked staff, or stick.

 From the story you can tell that

• hockey is played with a ball.
• the French have played hockey for a long time.
• hockey is now played by shepherds.
• hockey will not last much longer.

4. There weren’t always oranges in Europe. People from the Far East brought oranges to Europe during the middle Ages. Later, sailors from Europe brought oranges to America. Now the United States grows a million tons of oranges each year.

From the story you can tell that

• the first oranges probably grew in the Far East.
• orange trees make fruit only in winter .
• orange trees have blue flowers.
• there were many wars during the Middle Ages .
• Many people in the United States are sports fans. They like sports. Football is the sport most people like. The second most popular sport is fishing. There is one interesting thing about fishing. Fishing fans actually fish. From the story you can tell that most Americans
• play baseball and football.
• watch fishing contests .
• watch football or baseball games.

V. Assignment/ Homework

Draw conclusion for each of the following situation based on the Information given.

1. Father promised to take Glenn and Cecile to the mall.

•  Donna came home crying one afternoon. She was holding her report cared.

2. When mother came home. The door had been forced open in her home.

VI. Enrichment/ Remediation

For slow learners you can give simple exercises like the one below.

    Choose the best conclusion among the choices given after each situation.

•  Eden takes good care of her goldfish. Her grandmother came over

 One afternoon and put a spoonful of fish meal into the aquarium.

 Her goldfish died. Therefore, 

a. Grandmother gave the right amount of meal.

 b. Grandmother was excited upon seeing the goldfish.

c.  Grandmother gave the wrong amount of meal. 2. Eden was going to recite a poem in a school program. but when her

Name was called she forgot what she say.

a. Edna must felt happy.

b. Edna mastered her poem.

 c. Edna felt nervous tension and was embarrassed

3. Mary had measles. Carmen her sister ate the left over and slept with

her sister. What do you think will happen to Carmen? 

a. Carmen became strong.

 b. Carmen had a stomach ache.

 c. Carmen will get sick of measles too.

VII. Reinforcement

For the fast learners you can give a more advanced selection/ situation for them to draw/make conclusion.

Copy of the story:

The Old man and His Son

One old man was sitting with his 25 years old son in the train. Train is about to leave the station. All passengers are settling down their seat. All passengers are settling down their seat. As train started young man was filled with lot of joy and curiosity. He was sitting on the window side. He went out one hand and feeling the passing air. He shouted, “Papa see all trees are going behind”. Old man smile and admired son feelings. Beside the young man one couple was sitting and listing all the conversion between father and son. They were little awkward with the attitude of 25 years old man behaving like a small child. Suddenly young man again shouted, “Papa see the pond and animals. Clouds are moving with train”. Couple was watching the young man in embarrassingly.

Now its start raining and some of water drops touches the young man’s hand. He filled with joy and he closed the eyes. He shouted again,” Papa it’s raining, water is touching me, see papa”. Couple couldn’t help themselves and ask the old man. “Why don’t you visit the Doctor and get treatment for your son.” Old man said, “Yes, We are coming from the hospital as Today my son got his eye sight for first time in his life”.

Moral: “Don’t draw conclusions until you know all the facts.”

GROUP ACTIVITY Sheets

Group Activity 1

Read the situations and choose the best conclusion .

1.  Some schools already have computer assisted instruction. Computer classes are also being conducted in schools. Nowadays computer are being used in offices and Make communication fast and easy.

a. Office workers don’t use computers because it’s too expensive to buy.

b. Manipulations of computers are taught in schools because it is widely used in business and communication.

c. Computers are not important to the big business company.

 2. Children are now getting crazier over computer- generated Games, Therefore,

a. Excessive use/ play on the computer by children have bad/ negative effects on them.

a. All children now have access to computers.

b. Computer only has beneficial/ good effects on Children.

c. Computer only has beneficial/ good effects on Children.

3. One common superstition is the fear of the number 13. The fear of 13 shows up in many places. On most airplanes there is no thirteenth row of seats. Most tall buildings do not have a thirteenth floor. And many people feel a bit nervous on Friday the thirteenth.

                                                                                                                                                               From the story you cannot tell

a. that the fear of 13 is very common.

b. why people fear the number 13.

c. if airplanes use 13 as a row number.

d. on what day Friday the thirteenth falls.

Group Activity 2

Read the following passage and answer the questions. You will need to use the information given to draw conclusions based on what you know.

Mary went downstairs. There was an aroma in the air. The table was set with warm blueberry muffins, freshly squeezed orange juice, and brewed coffee. A note was left on the table. It said, “Dear Mary, Have a great day. Good Luck at your new job. Love, Dan” Next to the note there was a wrapped present with a bow on top.

1. Who is Mary?

A: a wife                        B: a husband

C: a baker                      D: a nice person

2. What time of day is it?

A: morning                      B: afternoon

3 . Based on the information in the passage, what does aroma probably mean?

A: something pretty       B: something nice

C: something that smells

4. There was brewed coffee on the table. What does brewed mean?

A: old                             B: made

5. What was the purpose of the note?

A: to inform                   B: to entertain

C: to congratulate D: to thank

Who is Dan?

A: her husband    C: her neighbor

6. What is Mary’s new job?

A: store clerk                   B: baker

C: real estate agent        D: not enough information given

7. Who prepared the muffins, orange juice, and, coffee?

A: Mary                         B: Dan

8. Why was there a present on the table?

A: to congratulate          B: a thank you gift

C: to say sorry              D: not enough information given

Group Activity 3

Read the situations and choose the best conclusion.

1. Almost everywhere there is condominium, a townhouse or an office building which is being built. It seems people are ready to buy these new age houses in the subdivision and invest them.

a. There is a boom in construction of building.

b. People have no interest to build or buy beautiful and new houses.

C. Our changing society should get to know about interest.

2. Almost everywhere there you see people bringing along their cellular phones and beepers. These gadgets definitely make people contact each other regardless of distance.

a. These gadgets definitely make communication faster and easier.

b. There is no progress in the Science of communication.

c. “Small-mail” sending of information still patronized by people.

3. There are now circumferential roads in manila, like the construction of skyway and the MRT (Metro Rail Transit). These are fly-over and bridges which are constructed.

a. These projects make traveling faster and more comfortable.

b. It seems that the traffic problem will not be solved.

c. These are not helpful projects to ease the heavy traffic of cars.

For more readings

• ing Verbs English Lesson and Exercises – ing Forms, Spelling Rules and Grammar
• Present Simple Verb Tense | Present simple English Verb
• Modal Verb Could – Form, Use and Meaning in English
• To and Towards – Confusing English words | Vocabulary
• Conjunctions “And” “But” “Or” and “So”- English Lesson For Beginners

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