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Published 2008 Revised 2019

Understanding Hypotheses

'What happens if ... ?' to ' This will happen if'

The experimentation of children continually moves on to the exploration of new ideas and the refinement of their world view of previously understood situations. This description of the playtime patterns of young children very nicely models the concept of 'making and testing hypotheses'. It follows this pattern:

• Make some observations. Collect some data based on the observations.
• Draw a conclusion (called a 'hypothesis') which will explain the pattern of the observations.
• Test out your hypothesis by making some more targeted observations.

So, we have

• A hypothesis is a statement or idea which gives an explanation to a series of observations.

Sometimes, following observation, a hypothesis will clearly need to be refined or rejected. This happens if a single contradictory observation occurs. For example, suppose that a child is trying to understand the concept of a dog. He reads about several dogs in children's books and sees that they are always friendly and fun. He makes the natural hypothesis in his mind that dogs are friendly and fun . He then meets his first real dog: his neighbour's puppy who is great fun to play with. This reinforces his hypothesis. His cousin's dog is also very friendly and great fun. He meets some of his friends' dogs on various walks to playgroup. They are also friendly and fun. He is now confident that his hypothesis is sound. Suddenly, one day, he sees a dog, tries to stroke it and is bitten. This experience contradicts his hypothesis. He will need to amend the hypothesis. We see that

• Gathering more evidence/data can strengthen a hypothesis if it is in agreement with the hypothesis.
• If the data contradicts the hypothesis then the hypothesis must be rejected or amended to take into account the contradictory situation.

• A contradictory observation can cause us to know for certain that a hypothesis is incorrect.
• Accumulation of supporting experimental evidence will strengthen a hypothesis but will never let us know for certain that the hypothesis is true.

In short, it is possible to show that a hypothesis is false, but impossible to prove that it is true!

Whilst we can never prove a scientific hypothesis to be true, there will be a certain stage at which we decide that there is sufficient supporting experimental data for us to accept the hypothesis. The point at which we make the choice to accept a hypothesis depends on many factors. In practice, the key issues are

• What are the implications of mistakenly accepting a hypothesis which is false?
• What are the cost / time implications of gathering more data?
• What are the implications of not accepting in a timely fashion a true hypothesis?

For example, suppose that a drug company is testing a new cancer drug. They hypothesise that the drug is safe with no side effects. If they are mistaken in this belief and release the drug then the results could have a disastrous effect on public health. However, running extended clinical trials might be very costly and time consuming. Furthermore, a delay in accepting the hypothesis and releasing the drug might also have a negative effect on the health of many people.

In short, whilst we can never achieve absolute certainty with the testing of hypotheses, in order to make progress in science or industry decisions need to be made. There is a fine balance to be made between action and inaction.

Hypotheses and mathematics So where does mathematics enter into this picture? In many ways, both obvious and subtle:

• A good hypothesis needs to be clear, precisely stated and testable in some way. Creation of these clear hypotheses requires clear general mathematical thinking.
• The data from experiments must be carefully analysed in relation to the original hypothesis. This requires the data to be structured, operated upon, prepared and displayed in appropriate ways. The levels of this process can range from simple to exceedingly complex.

Very often, the situation under analysis will appear to be complicated and unclear. Part of the mathematics of the task will be to impose a clear structure on the problem. The clarity of thought required will actively be developed through more abstract mathematical study. Those without sufficient general mathematical skill will be unable to perform an appropriate logical analysis.

Using deductive reasoning in hypothesis testing

There is often confusion between the ideas surrounding proof, which is mathematics, and making and testing an experimental hypothesis, which is science. The difference is rather simple:

• Mathematics is based on deductive reasoning : a proof is a logical deduction from a set of clear inputs.
• Science is based on inductive reasoning : hypotheses are strengthened or rejected based on an accumulation of experimental evidence.

Of course, to be good at science, you need to be good at deductive reasoning, although experts at deductive reasoning need not be mathematicians. Detectives, such as Sherlock Holmes and Hercule Poirot, are such experts: they collect evidence from a crime scene and then draw logical conclusions from the evidence to support the hypothesis that, for example, Person M. committed the crime. They use this evidence to create sufficiently compelling deductions to support their hypotheses beyond reasonable doubt . The key word here is 'reasonable'. There is always the possibility of creating an exceedingly outlandish scenario to explain away any hypothesis of a detective or prosecution lawyer, but judges and juries in courts eventually make the decision that the probability of such eventualities are 'small' and the chance of the hypothesis being correct 'high'.

• If a set of data is normally distributed with mean 0 and standard deviation 0.5 then there is a 97.7% certainty that a measurement will not exceed 1.0.
• If the mean of a sample of data is 12, how confident can we be that the true mean of the population lies between 11 and 13?

It is at this point that making and testing hypotheses becomes a true branch of mathematics. This mathematics is difficult, but fascinating and highly relevant in the information-rich world of today.

To read more about the technical side of hypothesis testing, take a look at What is a Hypothesis Test?

You might also enjoy reading the articles on statistics on the Understanding Uncertainty website

This resource is part of the collection Statistics - Maths of Real Life

Writing a Hypothesis & Prediction

A prediction and a hypothesis are different. However, experiments should include both a hypothesis and a prediction.

• A hypothesis is normally generated from an idea or observation.

Examples of hypotheses

• Adding water to a sunflower will help it grow.
• An increase in temperature will increase the rate of reaction.
• A change in pH will affect how an enzyme works.

• The prediction will explain how your hypothesis can be tested.
• The prediction states a relationship between two variables.
• The stated relationship should be suggested in the hypothesis.

Examples of predictions

• If I increase the amount of water I use to water the plant, it will grow more.
• If I decrease the temperature, the rate of reaction will decrease.
• If I increase the pH, the rate of activity will increase.

The word 'because'

• Once you have written the prediction, you can extend your work by using the word ‘because’.
• Use your scientific knowledge to explain your prediction.

1.1 Cells, Tissues & Organs

1.1.1 Microscopes

1.1.2 Magnification

1.1.3 Multicellular Organisms

1.1.4 Tissues

1.1.5 Organs

1.1.6 Unicellular Organisms

1.1.7 Diffusion

1.1.8 Factors Affecting Diffusion

1.1.9 Plant Cells

1.1.10 Cellulose

1.1.11 Plant Tissues

1.1.12 Leaves

1.1.13 Animal Cells

1.1.14 Comparing Animal & Plant Cells

1.1.15 How to Make a Model Animal and Plant Cell

1.1.16 Specialised Cells

1.1.17 Stem Cells

1.1.18 Uses of Stem Cells

1.1.19 Disadvantages of Stem Cells

1.1.20 Blood Components

1.1.21 Platelets

1.1.22 End of Topic Test - Cells & Organisation

1.1.23 The Lungs

1.1.24 Breathing

1.1.25 Plant Gas Exchange

1.1.26 Health

1.1.27 End of Topic Test - Living Organisms

1.2 Reproduction & Variation

1.2.1 Reproduction in Humans

1.2.2 Male Reproductive System

1.2.3 Female Reproductive System

1.2.4 Gestation

1.2.5 Pregnancy

1.2.6 Puberty

1.2.7 The Menstrual Cycle

1.2.8 Reproduction in Plants

1.2.9 Pollination

1.2.10 Dispersal Method

1.2.11 Variation

1.2.12 Causes of Variation

1.2.13 Inheritance

1.2.14 Adaptations and Evolution

1.2.15 Species & Selective Breeding

1.2.16 Genetic Conditions

1.2.17 End of Topic Test - Reproduction & Variation

1.3 Ecological Relationships & Classification

1.3.1 Species Interdependence

1.3.2 Food Chains & Webs

1.3.3 Changes to Food Webs

1.3.4 Relationships in an Ecosystem

1.3.5 The Impact of Environmental Change

1.3.6 Decomposers

1.3.7 Decay

1.3.8 Assessing Ecosystems

1.3.9 Ecological Sampling

1.3.10 Required Practical - Estimating Population Size

1.3.11 Pyramids of Number and Biomass

1.3.12 Classification of Living Organisms

1.3.13 Competition Between Organisms

1.3.14 Adaptations of Plants

1.3.15 Natural Selection

1.3.16 Evidence for Evolution

1.3.17 Environmental Changes & Extinctions

1.3.18 The Importance of Biodiversity

1.3.19 Bioaccumulation

1.3.20 End of Topic Test - Material Cycles & Energy

1.4 Digestion & Nutrition

1.4.1 Balanced Diets

1.4.2 Vitamins & Minerals

1.4.3 Protein

1.4.4 Lipids, Oils and Fats

1.4.5 Carbohydrates

1.4.6 Starch

1.4.7 Energy Needs

1.4.8 Dietary Fibre

1.4.9 Diseases Caused by Nutritional Deficiencies

1.4.10 Digestion

1.4.11 Plant Nutrition

1.4.12 Enzymes in Digestion

1.4.13 Required Practical - Enzymes in Digestion

1.5 Plants & Photosynthesis

1.5.1 Roots

1.5.2 Photosynthesis

1.5.3 Leaves

1.5.4 Rate of Photosynthesis

1.5.5 Testing the Rate of Photosynthesis

1.5.6 Water Transport in Plants

1.5.7 Translocation

1.5.8 The Carbon Cycle

1.5.9 Human Activities & Carbon Dioxide

1.6 Biological Systems & Processes

1.6.1 Living Organisms

1.6.2 Dichotomous Keys

1.6.3 Biomechanics

1.6.4 Muscles

1.6.5 The Skeleton

1.6.6 Measuring Forces

1.6.7 Antagonistic Muscle Pairings

1.6.8 The Respiratory System

1.6.9 Structure & Function of the Gas Exchange System

1.6.10 Breathing

1.6.11 Respiration

1.6.12 Respiration During Exercise

1.6.13 Anaerobic Respiration

1.6.14 Lactic Acid

1.6.15 Effects of Smoking on the Respiratory System

1.6.16 Balanced Diets

1.6.17 Human Growth & Development

1.6.19 Alleles

1.6.20 Genotype vs Phenotype

1.6.21 Punnett Squares

1.6.22 Joints

1.6.23 The Renal System

1.6.24 The Circulatory System

1.6.25 The Circulatory System

1.6.26 Glucose

1.6.27 Glucose and Diabetes

1.6.28 The Effects of Recreational Drug Use

1.6.29 Human Illnesses

1.6.30 Antibiotics

1.6.31 Vaccinations

1.6.32 How Antibiotics and Vaccines Work

1.6.33 Mental Health

2 Chemistry

2.1 Particles

2.1.1 Particles

2.1.2 States of Matter

2.1.3 Changes of State

2.1.4 Properties of States of Matter

2.1.5 Diffusion

2.1.6 Changing State

2.1.7 Pressure

2.1.8 Temperature Increase in a Gas

2.1.9 Conservation of Mass

2.1.10 Purity of Substances

2.1.11 Pure Substances

2.1.12 Evaporation

2.1.13 Mixtures

2.1.14 Separating Mixtures

2.1.15 Distillation

2.1.16 Chromatography

2.1.17 Solubility

2.1.18 Investigating Solubility

2.2 Chemical Reactions

2.2.1 Chemical Reactions

2.2.2 Common Reactions

2.2.3 Acids & Alkalis

2.2.4 Reactions of Acids

2.2.5 Testing for Hydrogen

2.2.6 The pH Scale

2.2.7 Titration

2.2.8 End of Topic Test - Chemical Reactions

2.3 Atoms, Elements, Compounds

2.3.1 Atoms

2.3.2 Elements

2.3.3 Compounds & Mixtures

2.3.4 Electron Configuration

2.3.5 Chemical Symbols

2.3.6 Chemical Formulae

2.3.7 Conservation of Mass

2.3.8 Vacuums

2.3.9 Molecules

2.3.10 End of Topic Test - Particles & Atoms

2.4 The Periodic Table

2.4.1 Physical Properties

2.4.2 Chemical Properties

2.4.3 The Periodic Table

2.4.4 Metals

2.4.5 Non-Metals

2.4.6 Alkali Metals

2.4.7 Halogens

2.4.8 Oxides

2.4.9 End of Topic Test - The Periodic Table

2.5 Materials & the Earth

2.5.1 The Composition of The Earth

2.5.2 The Structure of the Earth

2.5.3 Igneous Rocks

2.5.4 Sedimentary Rocks

2.5.5 Metamorphic Rocks

2.5.6 The Rock Cycle

2.5.7 Physical Weathering

2.5.8 Chemical Weathering

2.5.9 Biological Weathering

2.5.10 The Formation of Fossils

2.5.11 Crude Oil

2.5.12 End of Topic Test - Earth

2.5.13 The Earth's Early Atmosphere

2.5.14 The Earth's Atmosphere Today

2.5.15 Oxygen in the Atmosphere

2.5.16 Carbon Dioxide in the Atmosphere

2.5.17 Greenhouse Gases

2.5.18 Climate Change

2.5.19 Resources

2.5.20 Recycling

2.5.21 Ceramics

2.5.22 Polymers

2.5.23 Composites

2.5.24 End of Topic Test - Materials

2.5.25 End of Topic Test - Polymers

2.6 Reactivity

2.6.2 Ionic Bonding

2.6.3 State Symbols

2.6.4 Balancing Chemical Equations

2.6.5 Relative Formula Mass

2.6.6 Calculating the Relative Formula Mass

2.6.7 The Reactivity Series

2.6.8 Carbon & The Reactivity Series

2.6.9 Displacement Reactions

2.6.10 Displacement Reactions - Halogens

2.6.11 Alloys

2.6.12 Metal Alloys

2.7 Energetics

2.7.1 Measuring Gas Production

2.7.2 Observing a Colour Change

2.7.3 Analysing Reaction Rates

2.7.4 Factors Affecting the Rate of Reaction

2.7.5 Catalysts

2.7.6 Testing for Oxygen

2.7.7 Energy Changes During Reactions

2.8 Properties of Materials

2.8.1 Testing for Gases

2.8.2 Alloys

2.8.3 Density

2.8.4 Density of Solids, Liquids & Gases HyperLearning

3.1.1 Energy Stores & Pathways

3.1.2 Energy Transfers

3.1.3 Common Energy Transfers

3.1.4 Wasted Energy

3.1.5 Efficiency of Energy Transfer

3.1.6 Sankey Diagrams

3.1.7 Heat & Temperature

3.1.8 Heat Transfer

3.1.9 Conductors vs Insulators

3.1.10 Reducing Energy Transfers

3.1.11 Energy & Power

3.1.12 Energy in Food

3.1.13 Calories

3.1.14 Food Labels

3.1.15 Energy at Home

3.1.16 Fuel Bills

3.1.17 Calculating Fuel Bills

3.1.18 Non-Renewable Energy - Fossil Fuels

3.1.19 Other Non-Renewables

3.1.20 Renewable Energy - Air & Ground

3.1.21 Renewable Energy - Water

3.1.22 End of Topic Test - Energy

3.2 Forces & Motion

3.2.1 Forces

3.2.2 Contact Forces

3.2.3 Balanced Forces

3.2.4 Force Diagrams & Resultant Forces

3.2.5 Free Body Diagram - Uses

3.2.6 Force & Acceleration

3.2.7 Gravity

3.2.8 Weight

3.2.9 Pressure

3.2.10 Speed

3.2.11 Relative Motion

3.2.12 Friction

3.2.13 Water & Air Resistance

3.2.14 Distance-Time Graphs

3.2.15 Moments

3.2.16 Levers

3.2.17 Work

3.2.18 Machines

3.2.19 Work & Machines

3.2.20 Elasticity

3.2.21 Elasticity - Hooke's Law

3.2.22 Density

3.2.23 Floating & Sinking

3.2.24 End of Topic Test - Forces & Motion

3.2.25 Vacuums

3.2.26 Thermal Energy & Conduction

3.2.27 Convection & Radiation

3.2.28 Evaporation

3.3.1 Waves

3.3.2 Types of Waves

3.3.3 Observing Waves

3.3.4 Wave Speed

3.3.5 Earthquakes

3.3.6 Sound Waves

3.3.7 Uses of Sound Waves

3.3.8 The Interactions of Sound with Different Mediums

3.3.9 Reflecting Sounds

3.3.10 The Speed of Sound

3.3.11 Measuring the Speed of Sound

3.3.12 The Hearing Range of Humans

3.3.13 The Human Ear

3.3.14 Light Waves

3.3.15 Reflection

3.3.16 Drawing a Reflected Image

3.3.17 Refraction

3.3.18 The Human Eye

3.3.19 The Eye as a Pinhole Camera

3.3.20 Lenses

3.3.21 Colour

3.3.22 Seeing Colour

3.3.23 Colours of Light

3.3.24 Drawing Waves

3.3.25 Wave Interactions

3.3.26 Comparing Sound & Light

3.3.27 End of Topic Test - Waves

3.3.28 End of Topic Test - Sound

3.4 Electricity & Magnetism

3.4.1 Circuit Symbols

3.4.2 Resistors & Diodes

3.4.3 Electric Current

3.4.4 Measuring Current

3.4.5 Potential Difference

3.4.6 Series Circuits

3.4.7 Parallel Circuits

3.4.8 Resistance

3.4.9 Charges

3.4.10 Static Electricity

3.4.11 Magnets

3.4.12 Magnetic Fields

3.4.13 The Earth's Field

3.4.14 Electromagnetism

3.4.15 Uses of Electromagnets

3.4.16 Strength of Magnetic Fields

3.4.17 Circuit Symbols HyperLearning

3.5.1 Physical Reactions

3.5.2 Changes of State

3.5.3 Particles

3.5.4 Density

3.5.5 Density & the Particle Model

3.5.6 The Equation for Density

3.5.7 Dissolving

3.5.8 Brownian Motion

3.5.9 Diffusion

3.5.10 Filtration

3.5.11 Solids

3.5.12 Liquids

3.5.13 Gases

3.5.14 Weight & Mass

3.5.15 Gravity

3.5.16 Gravitational Field Strength

3.5.17 Gravity in Space

3.5.18 Atmospheric Pressure

3.5.19 Liquid Pressure

3.5.20 End of Topic Test - Matter

3.6 Space Physics

3.6.1 The Sun

3.6.2 The Planets

3.6.3 Other Astronomical Bodies

3.6.4 The Milky Way

3.6.5 Beyond The Milky Way

3.6.6 The Seasons

3.6.7 Days, Months & Years

3.6.8 The Moon

3.6.9 Light Years

3.6.10 End of Topic Test - Space

4 Thinking Scientifically

4.1 Models & Representations

4.1.1 Strengths & Limitations of Models

4.1.2 Symbols & Formulae to Represent Scientific Ideas

4.1.3 Analogies in Science

4.1.4 Changing Models – Atomic Theory

4.1.5 Working Safely in the Lab

4.1.6 Variables

4.1.7 Writing a Hypothesis & Prediction

4.1.8 Planning an Experiment

4.1.9 Maths Skills for Science

4.1.10 Drawing Scientific Apparatus

4.1.11 Observation & Measurement Skills

4.1.12 Types of Data

4.1.13 Graphs & Charts

4.1.14 Bias in Science

4.1.15 Conclude & Evaluate

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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What is a hypothesis?

No.  A hypothesis is sometimes described as an educated guess.  That's not the same thing as a guess and not really a good description of a hypothesis either.  Let's try working through an example.

If you put an ice cube on a plate and place it on the table, what will happen?  A very young child might guess that it will still be there in a couple of hours.  Most people would agree with the hypothesis that:

An ice cube will melt in less than 30 minutes.

You could put sit and watch the ice cube melt and think you've proved a hypothesis.  But you will have missed some important steps.

For a good science fair project you need to do quite a bit of research before any experimenting.  Start by finding some information about how and why water melts.  You could read a book, do a bit of Google searching, or even ask an expert.  For our example, you could learn about how temperature and air pressure can change the state of water.  Don't forget that elevation above sea level changes air pressure too.

Now, using all your research, try to restate that hypothesis.

An ice cube will melt in less than 30 minutes in a room at sea level with a temperature of 20C or 68F.

But wait a minute.  What is the ice made from?  What if the ice cube was made from salt water, or you sprinkled salt on a regular ice cube?  Time for some more research.  Would adding salt make a difference?  Turns out it does.  Would other chemicals change the melting time?

Using this new information, let's try that hypothesis again.

An ice cube made with tap water will melt in less than 30 minutes in a room at sea level with a temperature of 20C or 68F.

Does that seem like an educated guess?  No, it sounds like you are stating the obvious.

At this point, it is obvious only because of your research.  You haven't actually done the experiment.  Now it's time to run the experiment to support the hypothesis.

A hypothesis isn't an educated guess.  It is a tentative explanation for an observation, phenomenon, or scientific problem that can be tested by further investigation.

Once you do the experiment and find out if it supports the hypothesis, it becomes part of scientific theory.

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The scientific method - introductory lesson for KS3

Subject: Physics

Age range: 7-11

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Last updated

22 February 2018

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Scientific Hypotheses: Writing, Promoting, and Predicting Implications

Armen yuri gasparyan.

1 Departments of Rheumatology and Research and Development, Dudley Group NHS Foundation Trust (Teaching Trust of the University of Birmingham, UK), Russells Hall Hospital, Dudley, West Midlands, UK.

Lilit Ayvazyan

2 Department of Medical Chemistry, Yerevan State Medical University, Yerevan, Armenia.

Ulzhan Mukanova

3 Department of Surgical Disciplines, South Kazakhstan Medical Academy, Shymkent, Kazakhstan.

Marlen Yessirkepov

4 Department of Biology and Biochemistry, South Kazakhstan Medical Academy, Shymkent, Kazakhstan.

George D. Kitas

5 Arthritis Research UK Epidemiology Unit, University of Manchester, Manchester, UK.

Scientific hypotheses are essential for progress in rapidly developing academic disciplines. Proposing new ideas and hypotheses require thorough analyses of evidence-based data and predictions of the implications. One of the main concerns relates to the ethical implications of the generated hypotheses. The authors may need to outline potential benefits and limitations of their suggestions and target widely visible publication outlets to ignite discussion by experts and start testing the hypotheses. Not many publication outlets are currently welcoming hypotheses and unconventional ideas that may open gates to criticism and conservative remarks. A few scholarly journals guide the authors on how to structure hypotheses. Reflecting on general and specific issues around the subject matter is often recommended for drafting a well-structured hypothesis article. An analysis of influential hypotheses, presented in this article, particularly Strachan's hygiene hypothesis with global implications in the field of immunology and allergy, points to the need for properly interpreting and testing new suggestions. Envisaging the ethical implications of the hypotheses should be considered both by authors and journal editors during the writing and publishing process.

INTRODUCTION

We live in times of digitization that radically changes scientific research, reporting, and publishing strategies. Researchers all over the world are overwhelmed with processing large volumes of information and searching through numerous online platforms, all of which make the whole process of scholarly analysis and synthesis complex and sophisticated.

Current research activities are diversifying to combine scientific observations with analysis of facts recorded by scholars from various professional backgrounds. 1 Citation analyses and networking on social media are also becoming essential for shaping research and publishing strategies globally. 2 Learning specifics of increasingly interdisciplinary research studies and acquiring information facilitation skills aid researchers in formulating innovative ideas and predicting developments in interrelated scientific fields.

Arguably, researchers are currently offered more opportunities than in the past for generating new ideas by performing their routine laboratory activities, observing individual cases and unusual developments, and critically analyzing published scientific facts. What they need at the start of their research is to formulate a scientific hypothesis that revisits conventional theories, real-world processes, and related evidence to propose new studies and test ideas in an ethical way. 3 Such a hypothesis can be of most benefit if published in an ethical journal with wide visibility and exposure to relevant online databases and promotion platforms.

Although hypotheses are crucially important for the scientific progress, only few highly skilled researchers formulate and eventually publish their innovative ideas per se . Understandably, in an increasingly competitive research environment, most authors would prefer to prioritize their ideas by discussing and conducting tests in their own laboratories or clinical departments, and publishing research reports afterwards. However, there are instances when simple observations and research studies in a single center are not capable of explaining and testing new groundbreaking ideas. Formulating hypothesis articles first and calling for multicenter and interdisciplinary research can be a solution in such instances, potentially launching influential scientific directions, if not academic disciplines.

The aim of this article is to overview the importance and implications of infrequently published scientific hypotheses that may open new avenues of thinking and research.

Despite the seemingly established views on innovative ideas and hypotheses as essential research tools, no structured definition exists to tag the term and systematically track related articles. In 1973, the Medical Subject Heading (MeSH) of the U.S. National Library of Medicine introduced “Research Design” as a structured keyword that referred to the importance of collecting data and properly testing hypotheses, and indirectly linked the term to ethics, methods and standards, among many other subheadings.

One of the experts in the field defines “hypothesis” as a well-argued analysis of available evidence to provide a realistic (scientific) explanation of existing facts, fill gaps in public understanding of sophisticated processes, and propose a new theory or a test. 4 A hypothesis can be proven wrong partially or entirely. However, even such an erroneous hypothesis may influence progress in science by initiating professional debates that help generate more realistic ideas. The main ethical requirement for hypothesis authors is to be honest about the limitations of their suggestions. 5

EXAMPLES OF INFLUENTIAL SCIENTIFIC HYPOTHESES

Daily routine in a research laboratory may lead to groundbreaking discoveries provided the daily accounts are comprehensively analyzed and reproduced by peers. The discovery of penicillin by Sir Alexander Fleming (1928) can be viewed as a prime example of such discoveries that introduced therapies to treat staphylococcal and streptococcal infections and modulate blood coagulation. 6 , 7 Penicillin got worldwide recognition due to the inventor's seminal works published by highly prestigious and widely visible British journals, effective ‘real-world’ antibiotic therapy of pneumonia and wounds during World War II, and euphoric media coverage. 8 In 1945, Fleming, Florey and Chain got a much deserved Nobel Prize in Physiology or Medicine for the discovery that led to the mass production of the wonder drug in the U.S. and ‘real-world practice’ that tested the use of penicillin. What remained globally unnoticed is that Zinaida Yermolyeva, the outstanding Soviet microbiologist, created the Soviet penicillin, which turned out to be more effective than the Anglo-American penicillin and entered mass production in 1943; that year marked the turning of the tide of the Great Patriotic War. 9 One of the reasons of the widely unnoticed discovery of Zinaida Yermolyeva is that her works were published exclusively by local Russian (Soviet) journals.

The past decades have been marked by an unprecedented growth of multicenter and global research studies involving hundreds and thousands of human subjects. This trend is shaped by an increasing number of reports on clinical trials and large cohort studies that create a strong evidence base for practice recommendations. Mega-studies may help generate and test large-scale hypotheses aiming to solve health issues globally. Properly designed epidemiological studies, for example, may introduce clarity to the hygiene hypothesis that was originally proposed by David Strachan in 1989. 10 David Strachan studied the epidemiology of hay fever in a cohort of 17,414 British children and concluded that declining family size and improved personal hygiene had reduced the chances of cross infections in families, resulting in epidemics of atopic disease in post-industrial Britain. Over the past four decades, several related hypotheses have been proposed to expand the potential role of symbiotic microorganisms and parasites in the development of human physiological immune responses early in life and protection from allergic and autoimmune diseases later on. 11 , 12 Given the popularity and the scientific importance of the hygiene hypothesis, it was introduced as a MeSH term in 2012. 13

Hypotheses can be proposed based on an analysis of recorded historic events that resulted in mass migrations and spreading of certain genetic diseases. As a prime example, familial Mediterranean fever (FMF), the prototype periodic fever syndrome, is believed to spread from Mesopotamia to the Mediterranean region and all over Europe due to migrations and religious prosecutions millennia ago. 14 Genetic mutations spearing mild clinical forms of FMF are hypothesized to emerge and persist in the Mediterranean region as protective factors against more serious infectious diseases, particularly tuberculosis, historically common in that part of the world. 15 The speculations over the advantages of carrying the MEditerranean FeVer (MEFV) gene are further strengthened by recorded low mortality rates from tuberculosis among FMF patients of different nationalities living in Tunisia in the first half of the 20th century. 16

Diagnostic hypotheses shedding light on peculiarities of diseases throughout the history of mankind can be formulated using artefacts, particularly historic paintings. 17 Such paintings may reveal joint deformities and disfigurements due to rheumatic diseases in individual subjects. A series of paintings with similar signs of pathological conditions interpreted in a historic context may uncover mysteries of epidemics of certain diseases, which is the case with Ruben's paintings depicting signs of rheumatic hands and making some doctors to believe that rheumatoid arthritis was common in Europe in the 16th and 17th century. 18

WRITING SCIENTIFIC HYPOTHESES

There are author instructions of a few journals that specifically guide how to structure, format, and make submissions categorized as hypotheses attractive. One of the examples is presented by Med Hypotheses , the flagship journal in its field with more than four decades of publishing and influencing hypothesis authors globally. However, such guidance is not based on widely discussed, implemented, and approved reporting standards, which are becoming mandatory for all scholarly journals.

Generating new ideas and scientific hypotheses is a sophisticated task since not all researchers and authors are skilled to plan, conduct, and interpret various research studies. Some experience with formulating focused research questions and strong working hypotheses of original research studies is definitely helpful for advancing critical appraisal skills. However, aspiring authors of scientific hypotheses may need something different, which is more related to discerning scientific facts, pooling homogenous data from primary research works, and synthesizing new information in a systematic way by analyzing similar sets of articles. To some extent, this activity is reminiscent of writing narrative and systematic reviews. As in the case of reviews, scientific hypotheses need to be formulated on the basis of comprehensive search strategies to retrieve all available studies on the topics of interest and then synthesize new information selectively referring to the most relevant items. One of the main differences between scientific hypothesis and review articles relates to the volume of supportive literature sources ( Table 1 ). In fact, hypothesis is usually formulated by referring to a few scientific facts or compelling evidence derived from a handful of literature sources. 19 By contrast, reviews require analyses of a large number of published documents retrieved from several well-organized and evidence-based databases in accordance with predefined search strategies. 20 , 21 , 22

The format of hypotheses, especially the implications part, may vary widely across disciplines. Clinicians may limit their suggestions to the clinical manifestations of diseases, outcomes, and management strategies. Basic and laboratory scientists analysing genetic, molecular, and biochemical mechanisms may need to view beyond the frames of their narrow fields and predict social and population-based implications of the proposed ideas. 23

Advanced writing skills are essential for presenting an interesting theoretical article which appeals to the global readership. Merely listing opposing facts and ideas, without proper interpretation and analysis, may distract the experienced readers. The essence of a great hypothesis is a story behind the scientific facts and evidence-based data.

ETHICAL IMPLICATIONS

The authors of hypotheses substantiate their arguments by referring to and discerning rational points from published articles that might be overlooked by others. Their arguments may contradict the established theories and practices, and pose global ethical issues, particularly when more or less efficient medical technologies and public health interventions are devalued. The ethical issues may arise primarily because of the careless references to articles with low priorities, inadequate and apparently unethical methodologies, and concealed reporting of negative results. 24 , 25

Misinterpretation and misunderstanding of the published ideas and scientific hypotheses may complicate the issue further. For example, Alexander Fleming, whose innovative ideas of penicillin use to kill susceptible bacteria saved millions of lives, warned of the consequences of uncontrolled prescription of the drug. The issue of antibiotic resistance had emerged within the first ten years of penicillin use on a global scale due to the overprescription that affected the efficacy of antibiotic therapies, with undesirable consequences for millions. 26

The misunderstanding of the hygiene hypothesis that primarily aimed to shed light on the role of the microbiome in allergic and autoimmune diseases resulted in decline of public confidence in hygiene with dire societal implications, forcing some experts to abandon the original idea. 27 , 28 Although that hypothesis is unrelated to the issue of vaccinations, the public misunderstanding has resulted in decline of vaccinations at a time of upsurge of old and new infections.

A number of ethical issues are posed by the denial of the viral (human immunodeficiency viruses; HIV) hypothesis of acquired Immune deficiency Syndrome (AIDS) by Peter Duesberg, who overviewed the links between illicit recreational drugs and antiretroviral therapies with AIDS and refuted the etiological role of HIV. 29 That controversial hypothesis was rejected by several journals, but was eventually published without external peer review at Med Hypotheses in 2010. The publication itself raised concerns of the unconventional editorial policy of the journal, causing major perturbations and more scrutinized publishing policies by journals processing hypotheses.

WHERE TO PUBLISH HYPOTHESES

Although scientific authors are currently well informed and equipped with search tools to draft evidence-based hypotheses, there are still limited quality publication outlets calling for related articles. The journal editors may be hesitant to publish articles that do not adhere to any research reporting guidelines and open gates for harsh criticism of unconventional and untested ideas. Occasionally, the editors opting for open-access publishing and upgrading their ethics regulations launch a section to selectively publish scientific hypotheses attractive to the experienced readers. 30 However, the absence of approved standards for this article type, particularly no mandate for outlining potential ethical implications, may lead to publication of potentially harmful ideas in an attractive format.

A suggestion of simultaneously publishing multiple or alternative hypotheses to balance the reader views and feedback is a potential solution for the mainstream scholarly journals. 31 However, that option alone is hardly applicable to emerging journals with unconventional quality checks and peer review, accumulating papers with multiple rejections by established journals.

A large group of experts view hypotheses with improbable and controversial ideas publishable after formal editorial (in-house) checks to preserve the authors' genuine ideas and avoid conservative amendments imposed by external peer reviewers. 32 That approach may be acceptable for established publishers with large teams of experienced editors. However, the same approach can lead to dire consequences if employed by nonselective start-up, open-access journals processing all types of articles and primarily accepting those with charged publication fees. 33 In fact, pseudoscientific ideas arguing Newton's and Einstein's seminal works or those denying climate change that are hardly testable have already found their niche in substandard electronic journals with soft or nonexistent peer review. 34

CITATIONS AND SOCIAL MEDIA ATTENTION

The available preliminary evidence points to the attractiveness of hypothesis articles for readers, particularly those from research-intensive countries who actively download related documents. 35 However, citations of such articles are disproportionately low. Only a small proportion of top-downloaded hypotheses (13%) in the highly prestigious Med Hypotheses receive on average 5 citations per article within a two-year window. 36

With the exception of a few historic papers, the vast majority of hypotheses attract relatively small number of citations in a long term. 36 Plausible explanations are that these articles often contain a single or only a few citable points and that suggested research studies to test hypotheses are rarely conducted and reported, limiting chances of citing and crediting authors of genuine research ideas.

A snapshot analysis of citation activity of hypothesis articles may reveal interest of the global scientific community towards their implications across various disciplines and countries. As a prime example, Strachan's hygiene hypothesis, published in 1989, 10 is still attracting numerous citations on Scopus, the largest bibliographic database. As of August 28, 2019, the number of the linked citations in the database is 3,201. Of the citing articles, 160 are cited at least 160 times ( h -index of this research topic = 160). The first three citations are recorded in 1992 and followed by a rapid annual increase in citation activity and a peak of 212 in 2015 ( Fig. 1 ). The top 5 sources of the citations are Clin Exp Allergy (n = 136), J Allergy Clin Immunol (n = 119), Allergy (n = 81), Pediatr Allergy Immunol (n = 69), and PLOS One (n = 44). The top 5 citing authors are leading experts in pediatrics and allergology Erika von Mutius (Munich, Germany, number of publications with the index citation = 30), Erika Isolauri (Turku, Finland, n = 27), Patrick G Holt (Subiaco, Australia, n = 25), David P. Strachan (London, UK, n = 23), and Bengt Björksten (Stockholm, Sweden, n = 22). The U.S. is the leading country in terms of citation activity with 809 related documents, followed by the UK (n = 494), Germany (n = 314), Australia (n = 211), and the Netherlands (n = 177). The largest proportion of citing documents are articles (n = 1,726, 54%), followed by reviews (n = 950, 29.7%), and book chapters (n = 213, 6.7%). The main subject areas of the citing items are medicine (n = 2,581, 51.7%), immunology and microbiology (n = 1,179, 23.6%), and biochemistry, genetics and molecular biology (n = 415, 8.3%).

Interestingly, a recent analysis of 111 publications related to Strachan's hygiene hypothesis, stating that the lack of exposure to infections in early life increases the risk of rhinitis, revealed a selection bias of 5,551 citations on Web of Science. 37 The articles supportive of the hypothesis were cited more than nonsupportive ones (odds ratio adjusted for study design, 2.2; 95% confidence interval, 1.6–3.1). A similar conclusion pointing to a citation bias distorting bibliometrics of hypotheses was reached by an earlier analysis of a citation network linked to the idea that β-amyloid, which is involved in the pathogenesis of Alzheimer disease, is produced by skeletal muscle of patients with inclusion body myositis. 38 The results of both studies are in line with the notion that ‘positive’ citations are more frequent in the field of biomedicine than ‘negative’ ones, and that citations to articles with proven hypotheses are too common. 39

Social media channels are playing an increasingly active role in the generation and evaluation of scientific hypotheses. In fact, publicly discussing research questions on platforms of news outlets, such as Reddit, may shape hypotheses on health-related issues of global importance, such as obesity. 40 Analyzing Twitter comments, researchers may reveal both potentially valuable ideas and unfounded claims that surround groundbreaking research ideas. 41 Social media activities, however, are unevenly distributed across different research topics, journals and countries, and these are not always objective professional reflections of the breakthroughs in science. 2 , 42

Scientific hypotheses are essential for progress in science and advances in healthcare. Innovative ideas should be based on a critical overview of related scientific facts and evidence-based data, often overlooked by others. To generate realistic hypothetical theories, the authors should comprehensively analyze the literature and suggest relevant and ethically sound design for future studies. They should also consider their hypotheses in the context of research and publication ethics norms acceptable for their target journals. The journal editors aiming to diversify their portfolio by maintaining and introducing hypotheses section are in a position to upgrade guidelines for related articles by pointing to general and specific analyses of the subject, preferred study designs to test hypotheses, and ethical implications. The latter is closely related to specifics of hypotheses. For example, editorial recommendations to outline benefits and risks of a new laboratory test or therapy may result in a more balanced article and minimize associated risks afterwards.

Not all scientific hypotheses have immediate positive effects. Some, if not most, are never tested in properly designed research studies and never cited in credible and indexed publication outlets. Hypotheses in specialized scientific fields, particularly those hardly understandable for nonexperts, lose their attractiveness for increasingly interdisciplinary audience. The authors' honest analysis of the benefits and limitations of their hypotheses and concerted efforts of all stakeholders in science communication to initiate public discussion on widely visible platforms and social media may reveal rational points and caveats of the new ideas.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Gasparyan AY, Yessirkepov M, Kitas GD.
• Methodology: Gasparyan AY, Mukanova U, Ayvazyan L.
• Writing - original draft: Gasparyan AY, Ayvazyan L, Yessirkepov M.
• Writing - review & editing: Gasparyan AY, Yessirkepov M, Mukanova U, Kitas GD.

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Definition of hypothesis

Did you know.

The Difference Between Hypothesis and Theory

A hypothesis is an assumption, an idea that is proposed for the sake of argument so that it can be tested to see if it might be true.

In the scientific method, the hypothesis is constructed before any applicable research has been done, apart from a basic background review. You ask a question, read up on what has been studied before, and then form a hypothesis.

A hypothesis is usually tentative; it's an assumption or suggestion made strictly for the objective of being tested.

A theory , in contrast, is a principle that has been formed as an attempt to explain things that have already been substantiated by data. It is used in the names of a number of principles accepted in the scientific community, such as the Big Bang Theory . Because of the rigors of experimentation and control, it is understood to be more likely to be true than a hypothesis is.

In non-scientific use, however, hypothesis and theory are often used interchangeably to mean simply an idea, speculation, or hunch, with theory being the more common choice.

Since this casual use does away with the distinctions upheld by the scientific community, hypothesis and theory are prone to being wrongly interpreted even when they are encountered in scientific contexts—or at least, contexts that allude to scientific study without making the critical distinction that scientists employ when weighing hypotheses and theories.

The most common occurrence is when theory is interpreted—and sometimes even gleefully seized upon—to mean something having less truth value than other scientific principles. (The word law applies to principles so firmly established that they are almost never questioned, such as the law of gravity.)

This mistake is one of projection: since we use theory in general to mean something lightly speculated, then it's implied that scientists must be talking about the same level of uncertainty when they use theory to refer to their well-tested and reasoned principles.

The distinction has come to the forefront particularly on occasions when the content of science curricula in schools has been challenged—notably, when a school board in Georgia put stickers on textbooks stating that evolution was "a theory, not a fact, regarding the origin of living things." As Kenneth R. Miller, a cell biologist at Brown University, has said , a theory "doesn’t mean a hunch or a guess. A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”

While theories are never completely infallible, they form the basis of scientific reasoning because, as Miller said "to the best of our ability, we’ve tested them, and they’ve held up."

• proposition
• supposition

hypothesis , theory , law mean a formula derived by inference from scientific data that explains a principle operating in nature.

hypothesis implies insufficient evidence to provide more than a tentative explanation.

theory implies a greater range of evidence and greater likelihood of truth.

law implies a statement of order and relation in nature that has been found to be invariable under the same conditions.

Examples of hypothesis in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'hypothesis.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Greek, from hypotithenai to put under, suppose, from hypo- + tithenai to put — more at do

1641, in the meaning defined at sense 1a

Phrases Containing hypothesis

• counter - hypothesis
• nebular hypothesis
• null hypothesis
• planetesimal hypothesis
• Whorfian hypothesis

Articles Related to hypothesis

This is the Difference Between a...

This is the Difference Between a Hypothesis and a Theory

In scientific reasoning, they're two completely different things

Dictionary Entries Near hypothesis

hypothermia

hypothesize

Cite this Entry

“Hypothesis.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/hypothesis. Accessed 23 May. 2024.

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Hypotheses and Proofs

In this post

What is a hypothesis?

A hypothesis is basically a theory that somebody states that needs to be tested in order to see if it is true. Most of the time a hypothesis is a statement which someone claims is true and then a series of tests are made to see if the person is correct.

Hypothesis – a proposed true statement that acts as a starting point for further investigation.

Devising theories is how all scientists progress, not just mathematicians, and the evidence that is found must be collected and interpreted to see if it gives any light on the truth in the statement. Statistics can either prove or disprove a theory, which is why we need the evidence that we gather to be as close to the truth as possible: so that we can give an answer to the question with a high level of confidence.

Hypotheses are just the plural of a single hypothesis. A hypothesis is the first thing that someone must come up with when doing a test, as we must initially know what it is we wish to find out rather than blindly going into carrying out certain surveys and tests.

Some examples of hypotheses are shown below:

• Britain is colder than Spain
• A dog is faster than a cat
• Blondes have more fun
• The square of the hypotenuse of a triangle is equal to the sum of the squares of the other two sides

Obviously, some of these hypotheses are correct and others are not. Even though some may look wrong or right we still need to test the hypothesis either way to find out if it is true or false.

Some hypotheses may be easier to test than others, for example it is easy to test the last hypothesis above as this is very mathematical. However, when it comes to measuring something like ‘fun’ which is shown in the hypothesis ‘Blondes have more fun’ we will begin to struggle! How do you measure something like fun and in what units? This is why it is much easier to test certain hypotheses when compared with others.

Another way to come up with a hypothesis is by doing some ‘trial and error’ type testing. When finding data you may realise that there is in fact a pattern and then state this as a hypothesis of your findings. This pattern should then be tested using mathematical skills to test its authenticity. There is still a big difference between finding a pattern in something and finding that something will always happen no matter what. The pattern that is found at any point may just be a coincidence as it is much harder to prove something using mathematics rather than simply noticing a pattern. However, once something is proved with mathematics it is a very strong indication that the hypothesis is not only a guess but is scientific fact.

A hypothesis must always:

• Be a statement that needs to be proven or disproven, never a question
• Be applied to a certain population
• Be testable, otherwise the hypothesis is rather pointless as we can never know any information about it!

There are also two different types of hypothesis which are explained here:

An Experimental Hypothesis –  This is a statement which should state a difference between two things that should be tested. For example, ‘Cheetahs are faster than lions’.

A Null Hypothesis –  This kind of hypothesis does not say something is more than another, instead it states that they are the same. For example, ‘There is no difference between the number of late buses on Tuesday and on Wednesday’.

Subjects and samples

We have already talked in an earlier lesson of different types of samples and how these are formed, so we will not dwell for too long on this. The main thing to make sure of when choosing subjects for a test is to link them to the hypothesis that we are looking into. This will then give a much better data set that will be a lot more relevant to the questions we are asking. There is no point in us gathering data from people that live in Ireland if our original hypothesis states something about Scottish people, so we need to also make sure that the sample taken is as relevant to the hypothesis as possible. As with all samples that are taken, there should never be any bias towards one subject or another (unless we are using something like quota sampling as outlined in an earlier lesson). This will then mean that a random collection of subjects is taken into account and will mean that the information that is acquired will be more useful to the hypothesis that we wish to look at.

The experimental method

By treating the hypothesis and the data collection as an experiment, we should use as many scientific methods as possible to ensure that the data we are collecting is very accurate.

The most important and best way of doing this is the  control of variables . A variable is basically anything that can change in a situation, which means there are a lot in the vast majority as lots of different things can be altered. By keeping all variables the same and only changing the ones which we wish to test, we will get data that is as reliable as possible. However, if variables are changed that can affect an outcome we may end up getting false data.

For example, when testing ‘A cheetah is faster than a lion’ we could simply make the two animals run against each other and see which is quickest. However, if we allowed the cheetah to run on flat ground and made the lion run up hill, then the times would not be accurate to the truth as it is much harder to run up a slope than on flat ground. It is for this reason that any variables should be the same for all subjects.

The only variable that is mentioned in the hypothesis ‘A cheetah runs faster than a lion’ is the animal that runs. Therefore, this is called the  independent variable  and is the only thing that we wish to change between experiments as it is the thing we wish to  prove has an effect on other results.

A  dependent variable  is something that we wish to measure in experiments to see if there is an effect. This is the speed at which something runs in our example, as we are changing the animal and measuring the speed.

Independent variable – something that stands alone and is not changed by other variables in the experiment. This variable is changed by the person carrying out the investigation to see if it influences the dependent variables. This can also be seen as an input when an experiment is created.

Dependent variable – this variable is measured in an experiment to see if it changes when the independent variable is changed. These represent an output after the experiment is carried out.

Standardised instructions

Another thing that is essential to carrying out experiments is to give both of the participants the same instructions in what you wish them to do. Although this may seem a little picky, there will be a definite difference in how a subject performs if they are given clear and concise instructions as opposed to given misleading and rushed ones.

Turning data into information

Experiments are carried out to produce a set of data but this is not the end of the problem! We will then need to interpret and change this information into something that will tell us what we need to know. This means we need to turn data in the form of numbers into actual information that can be useful to our investigation. Figures that are found through experiments are first shown as ‘raw data’ before we can use different tables and charts to show the patterns that have been found in the surveys and experiments that have been carried out. Once all the data is collected and in tables we can move on to using these to find patterns.

Once a hypothesis has been stated, we can look to prove or disprove it. In mathematics, a proof is a little different to what people usually think. A mathematical proof must show that something is the case without any doubt. We do this by working through step-by-step to build a proof that shows the hypothesis as being either right or wrong. Each small step in the proof must be correct so that the entire thing cannot be argued.

Setting out a proof

Being able to write a proof does not mean that you must work any differently to how you would usually answer a question. It simply means that you must show that something is the case. Questions on proofs may ask you to ‘prove’, ‘verify’ or ‘check’ a statement.

When doing this you will need to first understand the hypothesis that has been stated. Look at the example below to see how we would go about writing a simple proof.

Prove that 81 is not a prime number.

Here we have a hypothesis that 81 is not prime. So, to prove this, we can try to find a factor of 81 that is not 1 as we know the definition of a prime number is that it is only divisible by itself and 1. Therefore, we could simply show that:

The fact that 81 divided by 9 gives us 9 proves the hypothesis that 81 is not prime.

A proof for a hypothesis does not have to be very complex – it simply has to show that a statement is either true or false. Doing this will use your problem-solving skills though, as you may need to think outside the box and ensure that all of the information that you have is fully understood.

Harder examples

Being able to prove something can be very challenging. It is true that some mathematical equations are still yet to be proved and many mathematicians work on solving extremely complex proofs every day.

When looking at harder examples of proofs you will need to find like terms in equations and then think about how you can work through the proof to get the desired result.

Here we need to use the left-hand side to get to the right-hand side in order to prove that they are equal. We can do this by expanding the brackets on the left and collecting the like terms:

We have now expanded the brackets and collected the like terms. It is now that we will need to look at our hypothesis again and try to make the above equation into the right-hand side by moving terms around. We can see from the right-hand side of our hypothesis that we have a double bracket and then 2 added to this so we can begin by bringing 2 out of the above:

So we have now worked through an entire proof from start to finish. Here it is again using only mathematics and no writing:

In the above we have shown that the hypothesis is true by working through step-by-step and rearranging the equation on the left to get the one on the right.

The step-by-step approach to proofs

To prove something is correct we have used a step-by-step approach so far. This method is a very good way to get from the left-hand side of an equation to the right-hand side through different steps. To do this we can use specific rules:

1) Try to multiply out brackets early on where possible.  This will help you to cancel out certain terms in order to simplify the equation.

3) Take small steps each time.  A proof is about working through a problem slowly so that it is easy to spot what has been done in each step. Do not take big leaps in your work such as multiplying out brackets and collecting like terms all at once. Remember that the person marking your paper needs to see your working, so it is good to work in small stages.

4) Go back and check your work.  Once you have finished your proof you can go back and check each individual stage. One of the good things about carrying out a proof is that you will know if a mistake has been made in your arithmetic because you will not be able to get to the final solution. If this happens, go back and check your working throughout.

Harder proofs

When working through a proof that is more difficult it can be quite tricky. Sometimes we may have to carry out a lot of different steps or even prove something using another piece of knowledge. For example, it might be that we are asked to prove that an expression will always be even or that it will always be positive.

In the above equation we have worked through to get an answer that is completely multiplied by 4. This must therefore be even as any number (whether even or odd) will be even when multiplied by 4.

In this example we have had to use our knowledge that anything multiplied by 4 must be even. This information was not included in the question but is something that we know from previous lessons. Some examples of information that you may need to know in order to solve more difficult proofs are:

Any number that is multiplied by an even number must be even

A number multiplied by an even number and then added to an odd number will be odd

Any number multiplied by a number will give an answer that is divisible by the same number (e.g. 3 n  must be divisible by 3)

Any number that is squared must be positive

Above we have come to an answer that is multiplied by 3. This means that the answer has to be divisible by 3 also.

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Lesson Plan: KS3 science – introducing practicals

• Subject: Maths and Science
• Date Posted: 27 September 2013
• View page as PDF: Download Now

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​Introduce your new Y7s to the thrilling world of scientific experimentation, with the help of Dr Joanna Rhodes inspiring suggestions…

​PRACTICAL MAGIC

Introduce your new Y7s to the thrilling world of scientific experimentation, with the help of Dr Joanna Rhodes inspiring suggestions…

TODAY YOU WILL…

+ LEARN ABOUT THE EXCITEMENT AND ROLE OF EXPERIMENTATION IN SCIENCE

+ LEARN HOW TO CARRY OUT EXPERIMENTS SAFELY AND ANALYSE AND PRESENT THE RESULTS

In walk my year 7 class. Full of excitement and energy, their first question is “Are we doing a practical today Miss?” Over the next few years as they move up through the school the question never changes. This plan is dedicated to turning your students into skilled scientists and experimenters. The focus is not simply on how to do practical work but how to use it for both discovery and verification of scientific facts and information. Used well, experiments can support the curriculum and lead to a deeper more sophisticated understanding that helps students to apply their knowledge.

In this lesson, students will start to understand the excitement of experimentation and the role of experiments in discovering and verifying scientific information. They will learn how to carry our experiments safely and how to obtain information from the experiment that supports or refutes a hypothesis. Pupils will learn about techniques to analyse information such as creating tables and plotting graphs and how to use computer equipment such as a data logger. Cross-curricular links are developed with other practical subjects and also history and English as we look at significant scientific discoveries and how modern discoveries are published and subjected to peer review.

STARTER ACTIVITY

ARE WE DOING A PRACTICAL TODAY?

Before students come into the laboratory set it out with stations containing a range of equipment that they will use over the year.

Good stations to use include a microscope and slides; Bunsen burner and metal salts for flame tests; power pack and leads with a bulb and resistor; measuring equipment with measuring cylinders, volumetric flasks, pipettes and a balance; and a clamp stand, spring and slotted masses. Before students begin to handle the equipment ask them to go to each station and carry out a mini risk assessment based on what they can see. It helps to encourage them to think of ‘Hazard, Risk, Precaution’: what could harm me, how could it harm me, what steps will I take to protect myself?

Allow students to feed back to each other in groups. Use the information generated to create some rules for the lab. Students will be more likely to buy into these having created them. Students then explore the laboratory in groups with a mini experiment to do at each station.

The activity allows students to become familiar with a range of equipment and it will also give them the excitement of anticipating some of the activities that they will be doing in future science lessons.

MAIN ACTIVITIES

MAKING DISCOVERIES

In this activity students investigate some major scientific discoveries. Ask them to log onto Factmonster [Additional Resource 1] and pick one of the summaries including: gravity; electricity; bacteria and health; evolution; the theory of relativity; the big bang theory; discovery of penicillin; and the structure of DNA. Students should then investigate their chosen theory, focusing on the experiments that scientists carried out. They should then produce a presentation. This could be a PowerPoint but encourage students to explore other ways of presenting, too, including acting out a short play of their own or using a scripted play from the ASE [AR2]; producing a Prezi [AR3] and delivering a TED style presentation [AR4]; or designing the front cover of a newspaper announcing the discovery with fabulous graphics from Make the Front Page [AR5].

TESTING A HYPOTHESIS

In this activity students come up with ways to test their own hypothesis. Examples include simple relationships between the height a ball is dropped from and the height it rebounds to; the size of nettle leaves growing in the sun or in shade; and the resistance of a light bulb and the current passing through it. Initially students should investigate what makes a good hypothesis, an example of how to do this can be found at Science Kids at Home [AR6]. They should then design an experiment using a model you have provided which could be the superb worksheet produced by Holt, Rinehart and Winston [AR7]. Developing students’ scientific literacy is a vital process and introducing new vocabulary about the variables they will be testing is appropriate at this stage.

Students should become familiar with the terms independent, dependent and control variable and how these relate to both the measurements they will make and how they will make the experiment a fair test. Science Buddies has a website to help with an excellent range of examples and descriptions in language that students will find easy to understand [AR8].

HOME LEARNING

Pitching for a prac!

The Nuffield Foundation [AR12] in partnership with the Institute of Physics, Royal Society of Chemistry and the Society of Biology has produced sheets for practical work. Give students the web address and ask them to find a practical that inspires them. They should produce a 3-minute pitch for the practical of their choice. Students can then vote for their top three experiments to do in lesson time or as a science club activity. This develops students’ own sense of discovery as they can investigate experiments that fascinate them.

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A statement that could be true, which might then be tested.

Example: Sam has a hypothesis that "large dogs are better at catching tennis balls than small dogs". We can test that hypothesis by having hundreds of different sized dogs try to catch tennis balls.

Sometimes the hypothesis won't be tested, it is simply a good explanation (which could be wrong). Conjecture is a better word for this.

Example: you notice the temperature drops just as the sun rises. Your hypothesis is that the sun warms the air high above you, which rises up and then cooler air comes from the sides.

Note: when someone says "I have a theory" they should say "I have a hypothesis", because in mathematics a theory is actually well proven.