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draw a conclusion

Definition of draw a conclusion

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“Draw a conclusion.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/draw%20a%20conclusion. Accessed 2 Jun. 2024.

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• 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.

If you've found this article valuable so far, you may also like our free tool

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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Draw a/the conclusion.

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a large amount of ice, snow, and rock falling quickly down the side of a mountain

Keeping up appearances (Talking about how things seem)

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Praxis Core Reading

Course: praxis core reading   >   unit 1.

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• Organization | Worked example
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Online Guide to Writing and Research

The research process, explore more of umgc.

• 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.

Mailing Address: 3501 University Blvd. East, Adelphi, MD 20783 This work is licensed under a  Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License . © 2022 UMGC. All links to external sites were verified at the time of publication. UMGC is not responsible for the validity or integrity of information located at external sites.

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

Writing Handbooks

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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In a short paper—even a research paper—you don’t need to provide an exhaustive summary as part of your conclusion. But you do need to make some kind of transition between your final body paragraph and your concluding paragraph. This may come in the form of a few sentences of summary. Or it may come in the form of a sentence that brings your readers back to your thesis or main idea and reminds your readers where you began and how far you have traveled.

So, for example, in a paper about the relationship between ADHD and rejection sensitivity, Vanessa Roser begins by introducing readers to the fact that researchers have studied the relationship between the two conditions and then provides her explanation of that relationship. Here’s her thesis: “While socialization may indeed be an important factor in RS, I argue that individuals with ADHD may also possess a neurological predisposition to RS that is exacerbated by the differing executive and emotional regulation characteristic of ADHD.”

In her final paragraph, Roser reminds us of where she started by echoing her thesis: “This literature demonstrates that, as with many other conditions, ADHD and RS share a delicately intertwined pattern of neurological similarities that is rooted in the innate biology of an individual’s mind, a connection that cannot be explained in full by the behavioral mediation hypothesis.”  

Highlight the “so what”  

At the beginning of your paper, you explain to your readers what’s at stake—why they should care about the argument you’re making. In your conclusion, you can bring readers back to those stakes by reminding them why your argument is important in the first place. You can also draft a few sentences that put those stakes into a new or broader context.

In the conclusion to her paper about ADHD and RS, Roser echoes the stakes she established in her introduction—that research into connections between ADHD and RS has led to contradictory results, raising questions about the “behavioral mediation hypothesis.”

She writes, “as with many other conditions, ADHD and RS share a delicately intertwined pattern of neurological similarities that is rooted in the innate biology of an individual’s mind, a connection that cannot be explained in full by the behavioral mediation hypothesis.”  

Leave your readers with the “now what”  

After the “what” and the “so what,” you should leave your reader with some final thoughts. If you have written a strong introduction, your readers will know why you have been arguing what you have been arguing—and why they should care. And if you’ve made a good case for your thesis, then your readers should be in a position to see things in a new way, understand new questions, or be ready for something that they weren’t ready for before they read your paper.

In her conclusion, Roser offers two “now what” statements. First, she explains that it is important to recognize that the flawed behavioral mediation hypothesis “seems to place a degree of fault on the individual. It implies that individuals with ADHD must have elicited such frequent or intense rejection by virtue of their inadequate social skills, erasing the possibility that they may simply possess a natural sensitivity to emotion.” She then highlights the broader implications for treatment of people with ADHD, noting that recognizing the actual connection between rejection sensitivity and ADHD “has profound implications for understanding how individuals with ADHD might best be treated in educational settings, by counselors, family, peers, or even society as a whole.”

To find your own “now what” for your essay’s conclusion, try asking yourself these questions:

• What can my readers now understand, see in a new light, or grapple with that they would not have understood in the same way before reading my paper? Are we a step closer to understanding a larger phenomenon or to understanding why what was at stake is so important?  
• What questions can I now raise that would not have made sense at the beginning of my paper? Questions for further research? Other ways that this topic could be approached?  
• Are there other applications for my research? Could my questions be asked about different data in a different context? Could I use my methods to answer a different question?  
• What action should be taken in light of this argument? What action do I predict will be taken or could lead to a solution?  
• What larger context might my argument be a part of?  

What to avoid in your conclusion  

• a complete restatement of all that you have said in your paper.  
• a substantial counterargument that you do not have space to refute; you should introduce counterarguments before your conclusion.  
• an apology for what you have not said. If you need to explain the scope of your paper, you should do this sooner—but don’t apologize for what you have not discussed in your paper.  
• fake transitions like “in conclusion” that are followed by sentences that aren’t actually conclusions. (“In conclusion, I have now demonstrated that my thesis is correct.”)
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Drawing Conclusions

For any research project and any scientific discipline, drawing conclusions is the final, and most important, part of the process.

This article is a part of the guide:

• Null Hypothesis
• Research Hypothesis
• Defining a Research Problem
• Selecting Method

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• 1 Scientific Method
• 2.1.1 Null Hypothesis
• 2.1.2 Research Hypothesis
• 2.2 Prediction
• 2.3 Conceptual Variable
• 3.1 Operationalization
• 3.2 Selecting Method
• 3.3 Measurements
• 3.4 Scientific Observation
• 4.1 Empirical Evidence
• 5.1 Generalization
• 5.2 Errors in Conclusion

Whichever reasoning processes and research methods were used, the final conclusion is critical, determining success or failure. If an otherwise excellent experiment is summarized by a weak conclusion, the results will not be taken seriously.

Success or failure is not a measure of whether a hypothesis is accepted or refuted, because both results still advance scientific knowledge.

Failure lies in poor experimental design, or flaws in the reasoning processes, which invalidate the results. As long as the research process is robust and well designed, then the findings are sound, and the process of drawing conclusions begins.

The key is to establish what the results mean. How are they applied to the world?

What Has Been Learned?

Generally, a researcher will summarize what they believe has been learned from the research, and will try to assess the strength of the hypothesis.

Even if the null hypothesis is accepted, a strong conclusion will analyze why the results were not as predicted. 

Theoretical physicist Wolfgang Pauli was known to have criticized another physicist’s work by saying, “it’s not only not right; it is not even wrong.”

While this is certainly a humorous put-down, it also points to the value of the null hypothesis in science, i.e. the value of being “wrong.” Both accepting or rejecting the null hypothesis provides useful information – it is only when the research provides no illumination on the phenomenon at all that it is truly a failure.

In observational research , with no hypothesis, the researcher will analyze the findings, and establish if any valuable new information has been uncovered. The conclusions from this type of research may well inspire the development of a new hypothesis for further experiments. 

Generating Leads for Future Research

However, very few experiments give clear-cut results, and most research uncovers more questions than answers.

The researcher can use these to suggest interesting directions for further study. If, for example, the null hypothesis was accepted, there may still have been trends apparent within the results. These could form the basis of further study, or experimental refinement and redesign.

Question: Let’s say a researcher is interested in whether people who are ambidextrous (can write with either hand) are more likely to have ADHD. She may have three groups – left-handed, right-handed and ambidextrous, and ask each of them to complete an ADHD screening.

She hypothesizes that the ambidextrous people will in fact be more prone to symptoms of ADHD. While she doesn’t find a significant difference when she compares the mean scores of the groups, she does notice another trend: the ambidextrous people seem to score lower overall on tests of verbal acuity. She accepts the null hypothesis, but wishes to continue with her research. Can you think of a direction her research could take, given what she has already learnt?

Answer: She may decide to look more closely at that trend. She may design another experiment to isolate the variable of verbal acuity, by controlling for everything else. This may eventually help her arrive at a new hypothesis: ambidextrous people have lower verbal acuity.

Evaluating Flaws in the Research Process

The researcher will then evaluate any apparent problems with the experiment. This involves critically evaluating any weaknesses and errors in the design, which may have influenced the results .

Even strict, ' true experimental ,' designs have to make compromises, and the researcher must be thorough in pointing these out, justifying the methodology and reasoning.

For example, when drawing conclusions, the researcher may think that another causal effect influenced the results, and that this variable was not eliminated during the experimental process . A refined version of the experiment may help to achieve better results, if the new effect is included in the design process.

In the global warming example, the researcher might establish that carbon dioxide emission alone cannot be responsible for global warming. They may decide that another effect is contributing, so propose that methane may also be a factor in global warming. A new study would incorporate methane into the model.

What are the Benefits of the Research?

The next stage is to evaluate the advantages and benefits of the research.

In medicine and psychology, for example, the results may throw out a new way of treating a medical problem, so the advantages are obvious.

In some fields, certain kinds of research may not typically be seen as beneficial, regardless of the results obtained. Ideally, researchers will consider the implications of their research beforehand, as well as any ethical considerations. In fields such as psychology, social sciences or sociology, it’s important to think about who the research serves and what will ultimately be done with the results.

For example, the study regarding ambidexterity and verbal acuity may be interesting, but what would be the effect of accepting that hypothesis? Would it really benefit anyone to know that the ambidextrous are less likely to have a high verbal acuity?

However, all well-constructed research is useful, even if it only strengthens or supports a more tentative conclusion made by prior research.

Suggestions Based Upon the Conclusions

The final stage is the researcher's recommendations based on the results, depending on the field of study. This area of the research process is informed by the researcher's judgement, and will integrate previous studies.

For example, a researcher interested in schizophrenia may recommend a more effective treatment based on what has been learnt from a study. A physicist might propose that our picture of the structure of the atom should be changed. A researcher could make suggestions for refinement of the experimental design, or highlight interesting areas for further study. This final piece of the paper is the most critical, and pulls together all of the findings into a coherent agrument.

The area in a research paper that causes intense and heated debate amongst scientists is often when drawing conclusions .

Sharing and presenting findings to the scientific community is a vital part of the scientific process. It is here that the researcher justifies the research, synthesizes the results and offers them up for scrutiny by their peers.

As the store of scientific knowledge increases and deepens, it is incumbent on researchers to work together. Long ago, a single scientist could discover and publish work that alone could have a profound impact on the course of history. Today, however, such impact can only be achieved in concert with fellow scientists.

Summary - The Strength of the Results

The key to drawing a valid conclusion is to ensure that the deductive and inductive processes are correctly used, and that all steps of the scientific method were followed.

Even the best-planned research can go awry, however. Part of interpreting results also includes the researchers putting aside their ego to appraise what, if anything went wrong. Has anything occurred to warrant a more cautious interpretation of results?

If your research had a robust design, questioning and scrutiny will be devoted to the experiment conclusion, rather than the methods.

Question: Researchers are interested in identifying new microbial species that are capable of breaking down cellulose for possible application in biofuel production. They collect soil samples from a particular forest and create laboratory cultures of every microbial species they discover there. They then “feed” each species a cellulose compound and observe that in all the species tested, there was no decrease in cellulose after 24 hours.

Read the following conclusions below and decide which of them is the most sound:

They conclude that there are no microbes that can break down cellulose.

They conclude that the sampled microbes are not capable of breaking down cellulose in a lab environment within 24 hours.

They conclude that all the species are related somehow.

They conclude that these microbes are not useful in the biofuel industry.

They conclude that microbes from forests don’t break down cellulose.

Answer: The most appropriate conclusion is number 2. As you can see, sound conclusions are often a question of not extrapolating too widely, or making assumptions that are not supported by the data obtained. Even conclusion number 2 will likely be presented as tentative, and only provides evidence given the limits of the methods used.

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How to Write a Conclusion for Research Papers (with Examples)

The conclusion of a research paper is a crucial section that plays a significant role in the overall impact and effectiveness of your research paper. However, this is also the section that typically receives less attention compared to the introduction and the body of the paper. The conclusion serves to provide a concise summary of the key findings, their significance, their implications, and a sense of closure to the study. Discussing how can the findings be applied in real-world scenarios or inform policy, practice, or decision-making is especially valuable to practitioners and policymakers. The research paper conclusion also provides researchers with clear insights and valuable information for their own work, which they can then build on and contribute to the advancement of knowledge in the field.

The research paper conclusion should explain the significance of your findings within the broader context of your field. It restates how your results contribute to the existing body of knowledge and whether they confirm or challenge existing theories or hypotheses. Also, by identifying unanswered questions or areas requiring further investigation, your awareness of the broader research landscape can be demonstrated.

Remember to tailor the research paper conclusion to the specific needs and interests of your intended audience, which may include researchers, practitioners, policymakers, or a combination of these.

Table of Contents

What is a conclusion in a research paper, summarizing conclusion, editorial conclusion, externalizing conclusion, importance of a good research paper conclusion, how to write a conclusion for your research paper, research paper conclusion examples.

• How to write a research paper conclusion with Paperpal? 

Frequently Asked Questions

A conclusion in a research paper is the final section where you summarize and wrap up your research, presenting the key findings and insights derived from your study. The research paper conclusion is not the place to introduce new information or data that was not discussed in the main body of the paper. When working on how to conclude a research paper, remember to stick to summarizing and interpreting existing content. The research paper conclusion serves the following purposes: 1

• Warn readers of the possible consequences of not attending to the problem.
• Recommend specific course(s) of action.
• Restate key ideas to drive home the ultimate point of your research paper.
• Provide a “take-home” message that you want the readers to remember about your study.

Types of conclusions for research papers

In research papers, the conclusion provides closure to the reader. The type of research paper conclusion you choose depends on the nature of your study, your goals, and your target audience. I provide you with three common types of conclusions:

A summarizing conclusion is the most common type of conclusion in research papers. It involves summarizing the main points, reiterating the research question, and restating the significance of the findings. This common type of research paper conclusion is used across different disciplines.

An editorial conclusion is less common but can be used in research papers that are focused on proposing or advocating for a particular viewpoint or policy. It involves presenting a strong editorial or opinion based on the research findings and offering recommendations or calls to action.

An externalizing conclusion is a type of conclusion that extends the research beyond the scope of the paper by suggesting potential future research directions or discussing the broader implications of the findings. This type of conclusion is often used in more theoretical or exploratory research papers.

Align your conclusion’s tone with the rest of your research paper. Start Writing with Paperpal Now!  

The conclusion in a research paper serves several important purposes:

• Offers Implications and Recommendations : Your research paper conclusion is an excellent place to discuss the broader implications of your research and suggest potential areas for further study. It’s also an opportunity to offer practical recommendations based on your findings.
• Provides Closure : A good research paper conclusion provides a sense of closure to your paper. It should leave the reader with a feeling that they have reached the end of a well-structured and thought-provoking research project.
• Leaves a Lasting Impression : Writing a well-crafted research paper conclusion leaves a lasting impression on your readers. It’s your final opportunity to leave them with a new idea, a call to action, or a memorable quote.

Writing a strong conclusion for your research paper is essential to leave a lasting impression on your readers. Here’s a step-by-step process to help you create and know what to put in the conclusion of a research paper: 2

• Research Statement : Begin your research paper conclusion by restating your research statement. This reminds the reader of the main point you’ve been trying to prove throughout your paper. Keep it concise and clear.
• Key Points : Summarize the main arguments and key points you’ve made in your paper. Avoid introducing new information in the research paper conclusion. Instead, provide a concise overview of what you’ve discussed in the body of your paper.
• Address the Research Questions : If your research paper is based on specific research questions or hypotheses, briefly address whether you’ve answered them or achieved your research goals. Discuss the significance of your findings in this context.
• Significance : Highlight the importance of your research and its relevance in the broader context. Explain why your findings matter and how they contribute to the existing knowledge in your field.
• Implications : Explore the practical or theoretical implications of your research. How might your findings impact future research, policy, or real-world applications? Consider the “so what?” question.
• Future Research : Offer suggestions for future research in your area. What questions or aspects remain unanswered or warrant further investigation? This shows that your work opens the door for future exploration.
• Closing Thought : Conclude your research paper conclusion with a thought-provoking or memorable statement. This can leave a lasting impression on your readers and wrap up your paper effectively. Avoid introducing new information or arguments here.
• Proofread and Revise : Carefully proofread your conclusion for grammar, spelling, and clarity. Ensure that your ideas flow smoothly and that your conclusion is coherent and well-structured.

Write your research paper conclusion 2x faster with Paperpal. Try it now!

Remember that a well-crafted research paper conclusion is a reflection of the strength of your research and your ability to communicate its significance effectively. It should leave a lasting impression on your readers and tie together all the threads of your paper. Now you know how to start the conclusion of a research paper and what elements to include to make it impactful, let’s look at a research paper conclusion sample.

How to write a research paper conclusion with Paperpal?

A research paper conclusion is not just a summary of your study, but a synthesis of the key findings that ties the research together and places it in a broader context. A research paper conclusion should be concise, typically around one paragraph in length. However, some complex topics may require a longer conclusion to ensure the reader is left with a clear understanding of the study’s significance. Paperpal, an AI writing assistant trusted by over 800,000 academics globally, can help you write a well-structured conclusion for your research paper. 

• Sign Up or Log In: Create a new Paperpal account or login with your details.  
• Navigate to Features : Once logged in, head over to the features’ side navigation pane. Click on Templates and you’ll find a suite of generative AI features to help you write better, faster.  
• Generate an outline: Under Templates, select ‘Outlines’. Choose ‘Research article’ as your document type.  
• Select your section: Since you’re focusing on the conclusion, select this section when prompted.  
• Choose your field of study: Identifying your field of study allows Paperpal to provide more targeted suggestions, ensuring the relevance of your conclusion to your specific area of research. 
• Provide a brief description of your study: Enter details about your research topic and findings. This information helps Paperpal generate a tailored outline that aligns with your paper’s content. 
• Generate the conclusion outline: After entering all necessary details, click on ‘generate’. Paperpal will then create a structured outline for your conclusion, to help you start writing and build upon the outline.  
• Write your conclusion: Use the generated outline to build your conclusion. The outline serves as a guide, ensuring you cover all critical aspects of a strong conclusion, from summarizing key findings to highlighting the research’s implications. 
• Refine and enhance: Paperpal’s ‘Make Academic’ feature can be particularly useful in the final stages. Select any paragraph of your conclusion and use this feature to elevate the academic tone, ensuring your writing is aligned to the academic journal standards. 

By following these steps, Paperpal not only simplifies the process of writing a research paper conclusion but also ensures it is impactful, concise, and aligned with academic standards. Sign up with Paperpal today and write your research paper conclusion 2x faster .  

The research paper conclusion is a crucial part of your paper as it provides the final opportunity to leave a strong impression on your readers. In the research paper conclusion, summarize the main points of your research paper by restating your research statement, highlighting the most important findings, addressing the research questions or objectives, explaining the broader context of the study, discussing the significance of your findings, providing recommendations if applicable, and emphasizing the takeaway message. The main purpose of the conclusion is to remind the reader of the main point or argument of your paper and to provide a clear and concise summary of the key findings and their implications. All these elements should feature on your list of what to put in the conclusion of a research paper to create a strong final statement for your work.

A strong conclusion is a critical component of a research paper, as it provides an opportunity to wrap up your arguments, reiterate your main points, and leave a lasting impression on your readers. Here are the key elements of a strong research paper conclusion: 1. Conciseness : A research paper conclusion should be concise and to the point. It should not introduce new information or ideas that were not discussed in the body of the paper. 2. Summarization : The research paper conclusion should be comprehensive enough to give the reader a clear understanding of the research’s main contributions. 3 . Relevance : Ensure that the information included in the research paper conclusion is directly relevant to the research paper’s main topic and objectives; avoid unnecessary details. 4 . Connection to the Introduction : A well-structured research paper conclusion often revisits the key points made in the introduction and shows how the research has addressed the initial questions or objectives. 5. Emphasis : Highlight the significance and implications of your research. Why is your study important? What are the broader implications or applications of your findings? 6 . Call to Action : Include a call to action or a recommendation for future research or action based on your findings.

The length of a research paper conclusion can vary depending on several factors, including the overall length of the paper, the complexity of the research, and the specific journal requirements. While there is no strict rule for the length of a conclusion, but it’s generally advisable to keep it relatively short. A typical research paper conclusion might be around 5-10% of the paper’s total length. For example, if your paper is 10 pages long, the conclusion might be roughly half a page to one page in length.

In general, you do not need to include citations in the research paper conclusion. Citations are typically reserved for the body of the paper to support your arguments and provide evidence for your claims. However, there may be some exceptions to this rule: 1. If you are drawing a direct quote or paraphrasing a specific source in your research paper conclusion, you should include a citation to give proper credit to the original author. 2. If your conclusion refers to or discusses specific research, data, or sources that are crucial to the overall argument, citations can be included to reinforce your conclusion’s validity.

The conclusion of a research paper serves several important purposes: 1. Summarize the Key Points 2. Reinforce the Main Argument 3. Provide Closure 4. Offer Insights or Implications 5. Engage the Reader. 6. Reflect on Limitations

Remember that the primary purpose of the research paper conclusion is to leave a lasting impression on the reader, reinforcing the key points and providing closure to your research. It’s often the last part of the paper that the reader will see, so it should be strong and well-crafted.

• Makar, G., Foltz, C., Lendner, M., & Vaccaro, A. R. (2018). How to write effective discussion and conclusion sections. Clinical spine surgery, 31(8), 345-346.
• Bunton, D. (2005). The structure of PhD conclusion chapters.  Journal of English for academic purposes ,  4 (3), 207-224.

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Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

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Reading Worksheets, Spelling, Grammar, Comprehension, Lesson Plans

Drawing Conclusions Worksheets

These drawing conclusion worksheets ask the student to evaluate details and make a judgment. Learning to draw conclusions as they read is an important tool for students, as it aids in comprehending text. Click on the worksheet title to view the details and download a free, printable worksheet activity. Be sure to check out all of our reading worksheets .

Drawing a Conclusion

Your student will draw a conclusion based on clues in the text.

This worksheet on drawing conclusions will take your student into the realm of fantasy.

Drawing Conclusions from Pictures

Help your student understand inference with this visual worksheet.

What Conclusion Can You Make?

These sentences will help your student learn to draw conclusions.

What Is It?

Clues and conclusions are the focus of this worksheet.

Draw a Conclusion: Visual Details

Your student will use visual clues to make inferences about a group of pictures in this worksheet.

Novak Djokovic and Iga Swiatek remain the last former champions left in French Open 2024 singles draw after 2R concludes with major upsets

N ovak Djokovic and Iga Swiatek find themselves as the only remaining champions at the 2024 French Open in their respective draws after the conclusion of the first two rounds. Both the men's and the women's draw had three former champions each at the start of the tournament

This year's French Open has seen a lot of drama within the opening two rounds. A lot of blockbuster matches in the early stages of the tournament have made it highly engaging for tennis fans. Rafael Nadal vs Alexander Zverev, Stan Wawrinka vs Andy Murray, and Iga Swiatek vs Naomi Osaka, to name a few, were the ones that grabbed attention.

However, top names playing each other so early in the tournament led to several star players exiting the Paris Major by the end of two rounds. The early blockbuster matches have resulted in only one former champion remaining, each in the men's and women's draws.

Coincidentally, the two are also the defending champions. In the men's draw, despite his dip in form, Novak Djokovic has had a smooth failing so far defeating Pierre-Hugues Herbert and Roberto Carballes Baena in straight sets. On the other hand, Iga Swiatek eliminated Leolia Jeanjean and survived Naomi Osaka's inspired performance in the second round to go through successfully.

The men's draw had two other former champions - 14-time winner Rafael Nadal and 2015 winner Stan Wawrinka. While the Spaniard fell to Alexander Zverev in the opening round, Wawrinka, who ousted Andy Murray in the opener, was defeated by Pavel Kotov in the second round.

The women's draw also had two other former champions - 2021 champion Barbora Krejcikova and 2017 champion Jelena Ostapenko . Krejcikova lost in the opening round to Viktorija Golubic whereas Ostapenko was defeated by Clara Tauson in a three-set marathon.

Novak Djokovic will face Lorenzo Musetti next, Iga Swiatek set to play Marie Bouzkova

Novak Djokovic will continue his quest for a record-extending 25th Grand Slam as he takes on a tough challenge in Lorenzo Musetti next. Though the Serb leads their head-to-head 4-1, the Italian has been great so far with wins over Daniel Galan and home favorite and veteran Gael Monfils and is yet to drop a set.

On the other hand, Iga Swiatek , who is chasing to complete a three-peat at the French Open, will face Marie Bouzkova next in what will be their first meeting. Bouzkova has played sublime tennis so far at Roland Garros, comfortably defeating 28th seed Veronika Kudermatova and then Jana Fett in her first two rounds, winning both in straight sets.

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Google’s ai overview is flawed by design, and a new company blog post hints at why, google: "there are bound to be some oddities and errors" in system that told people to eat rocks..

Benj Edwards - May 31, 2024 7:47 pm UTC

On Thursday, Google capped off a rough week of providing inaccurate and sometimes dangerous answers through its experimental AI Overview feature by authoring a follow-up blog post titled, "AI Overviews: About last week." In the post, attributed to Google VP Liz Reid, head of Google Search, the firm formally acknowledged issues with the feature and outlined steps taken to improve a system that appears flawed by design, even if it doesn't realize it is admitting it.

Further Reading

To recap, the AI Overview feature—which the company showed off at Google I/O a few weeks ago—aims to provide search users with summarized answers to questions by using an AI model integrated with Google's web ranking systems. Right now, it's an experimental feature that is not active for everyone, but when a participating user searches for a topic, they might see an AI-generated answer at the top of the results, pulled from highly ranked web content and summarized by an AI model.

While Google claims this approach is "highly effective" and on par with its Featured Snippets in terms of accuracy, the past week has seen numerous examples of the AI system generating bizarre, incorrect, or even potentially harmful responses, as we detailed in a recent feature where Ars reporter Kyle Orland replicated many of the unusual outputs.

Drawing inaccurate conclusions from the web

Given the circulating AI Overview examples, Google almost apologizes in the post and says, "We hold ourselves to a high standard, as do our users, so we expect and appreciate the feedback, and take it seriously." But Reid, in an attempt to justify the errors, then goes into some very revealing detail about why AI Overviews provides erroneous information:

AI Overviews work very differently than chatbots and other LLM products that people may have tried out. They’re not simply generating an output based on training data. While AI Overviews are powered by a customized language model, the model is integrated with our core web ranking systems and designed to carry out traditional “search” tasks, like identifying relevant, high-quality results from our index. That’s why AI Overviews don’t just provide text output, but include relevant links so people can explore further. Because accuracy is paramount in Search, AI Overviews are built to only show information that is backed up by top web results. This means that AI Overviews generally don't “hallucinate” or make things up in the ways that other LLM products might.

Here we see the fundamental flaw of the system: "AI Overviews are built to only show information that is backed up by top web results." The design is based on the false assumption that Google's page-ranking algorithm favors accurate results and not SEO-gamed garbage. Google Search has been broken for some time , and now the company is relying on those gamed and spam-filled results to feed its new AI model.

Even if the AI model draws from a more accurate source, as with the 1993 game console search seen above, Google's AI language model can still make inaccurate conclusions about the "accurate" data, confabulating erroneous information in a flawed summary of the information available.

Generally ignoring the folly of basing its AI results on a broken page-ranking algorithm, Google's blog post instead attributes the commonly circulated errors to several other factors, including users making nonsensical searches "aimed at producing erroneous results." Google does admit faults with the AI model, like misinterpreting queries, misinterpreting "a nuance of language on the web," and lacking sufficient high-quality information on certain topics. It also suggests that some of the more egregious examples circulating on social media are fake screenshots.

"Some of these faked results have been obvious and silly," Reid writes. "Others have implied that we returned dangerous results for topics like leaving dogs in cars, smoking while pregnant, and depression. Those AI Overviews never appeared. So we’d encourage anyone encountering these screenshots to do a search themselves to check."

(No doubt some of the social media examples are fake, but it's worth noting that any attempts to replicate those early examples now will likely fail because Google will have manually blocked the results. And it is potentially a testament to how broken Google Search is if people believed extreme fake examples in the first place.)

While addressing the "nonsensical searches" angle in the post, Reid uses the example search, " How many rocks should I eat each day ," which went viral in a tweet on May 23. Reid says, "Prior to these screenshots going viral, practically no one asked Google that question." And since there isn't much data on the web that answers it, she says there is a "data void" or "information gap" that was filled by satirical content found on the web, and the AI model found it and pushed it as an answer, much like Featured Snippets might. So basically, it was working exactly as designed.

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Draw your own conclusions on Trump

Most of us will never be a juror in a criminal court, but like it or not we are all in the court of public opinion.

In that court we are not constricted to “beyond reasonable doubt,” so all those waiting for a verdict in Donald Trump’s cases are possibly hiding behind a shield because they want to. Just because someone is found not guilty in criminal court that for sure does not mean they are innocent. They are two totally different things, as the O J Simpson case proved.  We bend over backwards to make sure we don’t convict innocent people and, sadly, guilty people can walk away because of that.  That’s our system but it should not stop is from using our common sense to make our own decision.

Vincent Curcuru, Simsbury

Using headlights helps visibility

It’s afternoon and you’re driving on Interstate 91. It’s raining, there’s light fog, and heavy spray is cast from nearby trucks.

Your vehicle is light gray. Your headlights are not on. You are invisible.

Truly invisible. You blend in with the mist and the spray and the rain, even during daylight hours.

You are invisible. I strongly suspect that if you were in an accident, you’d be considered at fault even if you weren’t at fault, because other drivers can’t be liable if they can’t see you.

Turn on your wipers, turn on your headlights. It’s the law.

M. Regina Cram, Glastonbury

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Bring back GDKP -- call from a guild master and raid leader

Lots of players quit since P2 baucase of the GDKP banning. How can I draw the conclusion since there are other changes? Because there are many guildies who told me they leave because of the GDKP banning. Because the remaining guildies are compaining the GDKP bannning. My guild still has 5-6 ST raids every week on Lone Wolf. I am the guild master and the leader of two raids. I would assume I have more data compared to a normal player. Now we have lowest number of players. Since you can’t offer us P4 at the moment, just bring back GDKP. This may not increase the active player from 100 to 150, but probably will bring back 10-20% palyers. Anyway, Aggrend did make a poor decision.

in a mcdonalds fueled rage aggrend decided to ban gdkp due to his own incompetence of failing to ban rmt/gold buyers and people say that blizzard made a good decision by making it so people have to play the way they want you to, very shameful

bring it back already, it was dumb to pander to all the casuals who have now left the game already

Yeah, bring it back.

Then make any tracked rmt or gold bot activity an unappealable auto ban for the account.

bring back the best wow loot system

No swipe for you.

No gear for you.

we had over 300k unique characters running gnomer in p2 when GDKP was banned the issue is 20 mans and the extreme amount of time it takes to pug a group. no to GDKP sry to burst your bubble mate

but by your logic the GDKP’s quit because they cant GDKP anymore. which is it casuals or GDKP? Or is it neither and your a moron because you don’t see 20 mans killed the scene?

Small brain can only make one conclusion.

Gdkp’s were banned due to the outcry of casuals, so gdkp players quit. Casuals always quit, and they did so. Now that both those player bases are gone, you can bring one back. Can you take a guess how that would be done? I can give you multiple choice if you’d like.

Raid size wasn’t the issue, it was raid difficulty. If ST was the same as bfd, but a 20 man. The pug potential would be 20x higher and those casuals wiping on Shade would still be raid logging and getting a full clear every week. They aren’t good at the game, they have small friend groups, and pugs weren’t working.

No GDKP’s were banned because they were laundering illicit gold.

it is you could pug a group for BFD or gnomer in 15 min now it takes over an hour because people are impatient and leave after 15 min of waiting and not having a full group.

Thats because the player base has shrunk to almost nothing, how is that not the obvious reason?

In 2024, where you can buy a wow token in classic wow. This is the most delusional take of all time. The auction house launders thousands of gold an hour, wheres the outcry? Casuals hated gdkp because they weren’t allowed in. They had no gold, they had no parses, and they couldn’t stand being told no.

this was happening at the start of the patch your derp

Totally not anecdotal, ok.

hey i think you forgot to reply to one of the points

You shouldn’t be able to buy BOP gear… it’s that simple

Yet another vain attempt of gold buyers wanting to bring back their precious PTW.

But keep spamming these threads my good man, keep on.

Oh, 300k. That is a lot compared to 500k in p1. Even the week before p2 release, we have 400k. Imaging the player number dropping instead of raising at the release of a new patch.

Bringing back GDKP will not increase the quality of the game.

Most SoD players support the GDKP ban, so that’s not the reason they are leaving. P3 has had a lot of challenges and issues, and Cata was just released.

Nope, that’s not why. What now?

Nine’s Wide World of Sports

Aussie Super Rugby clubs learn their finals fate, who plays who in next weekend's quarter-finals

Australia will field three teams in the Super Rugby Pacific quarter-finals after the regular season reached a thrilling round 15 conclusion on Saturday night.

The third placed Brumbies are again Australia's leading light and will host the sixth placed Highlanders in Canberra next Saturday (7.35pm kickoff AEST).

The fifth placed Reds will travel to Hamilton to take on the fourth placed Chiefs on Friday (5.05pm kickoff AEST) while the eighth placed Rebels will face the minor premier Hurricanes in Wellington on Saturday (2.35pm kickoff AEST).

Watch all the action from the 2024 Super Rugby Pacific season with every match ad free, live and on demand on Stan Sport

The second placed Blues will host the seventh placed Drua in Auckland on Saturday (5.05pm kickoff AEST) after they were denied a bonus point by the Chiefs in a "breathtaking" finish at Eden Park in the final round.

Australia has not won a Super Rugby title since the Waratahs in 2014.

The seven time defending champion Crusaders were the big losers in the last, hectic scramble for playoff places, missing out on the quarter-finals for the first time in a decade.

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The Crusaders won their last regular season match 43-10 against Moana Pasifika on Friday to move inside the top eight playoff zone for the first time this season.

But they were shuffled back to ninth place when the Drua took a bonus point from a 40-19 win over the Rebels in Lautoka on Saturday.

The Drua moved up to seventh, reaching the quarter-finals for the second consecutive year, and the Rebels held on to eighth to qualify for the playoffs for the first time in their 14 year history.

But there was a sombre note to the achievement after Rugby Australia announced on Thursday that it will not renew the Rebels' licence to play in Super Rugby next season because of financial instability.

Saturday's match was the Rebels' last regular season match in Super Rugby and the club will cease to exist as soon as their playoff run ends.

The Crusaders needed the Rebels to beat or draw to progress to the playoffs themselves.

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The Crusaders issued a statement announcing a review of the season when their match in Fiji ended.

"Our review is focused on performance and ensures we don't make any rash decisions," said chief executive Colin Mainsbridge.

"We want the team and organisation to operate to the standard we expect and that every opportunity to improve is looked at."

Salesi Rayasi of the Hurricanes celebrates with Billy Proctor after scoring a try.  Getty

The Hurricanes also took a bonus point from a 41-14 win over the Highlanders in a match played in gale force winds in Wellington.

The bonus point broke the top of the table tie between the Hurricanes and the Blues, who beat the Chiefs 31-17 on Saturday.

The Blues and Hurricanes came into the last regular season round tied on 51 points.

The Hurricanes' bonus point win took them to 56 points while the Blues finished with 55 after missing a bonus point when the Chiefs scored a last minute try.

Winger Salesi Rayasi scored a hat trick of tries and backrower Du'Plessis Kirifi claimed two in an impressive Hurricanes win.

Top seeding means they will retain home advantage as long as they stay in the playoffs.

"I guess we just want to be able to control our own destiny and we're looking no further ahead than next week," Hurricanes captain Jordie Barrett said.

The Blues seemed to have a bonus point under wraps when they led 31-7, by five tries to one, midway through the second half.

But the Chiefs finished strongly with tries from Josh Ioane and Quinn Tupaea.

Josh Ioane of the Chiefs scores a try at Eden Park.  Getty

"We can be happy with the win but our goal was to win with the bonus point and unfortunately we didn't do that," Blues captain Patrick Tuipulotou said.

"It's very disappointing for us, especially with the way we wanted to play. We didn't do it consistently, only in patches."

The match in Lautoka was the most momentous of the round, deciding the playoff fate of the Drua, Rebels and Crusaders.

The game was locked 19-19 at halftime but the Drua shocked the Rebels with three tries immediately after the break to take it by six tries to three.

The Rebels scored three first half tries, including a penalty try for a professional foul by Drua scrumhalf Frank Lomani, who received a yellow card in his first match back from a six week suspension.

Lomani atoned by scoring one of the Drua's second half tries while winger Waqa Nalaga scored a try in each half.

Centre Iosefo Masi also scored a try to top an outstanding performance.

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