Choosing a Hypothesis test

It can be difficult to know what analysis to perform on your dataset. This page is designed to help you.

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Before you choose a test

Before you can choose how to analyse your data, there are three things you will need to understand:

1. What is a Hypothesis test?

Many statistical analyses are hypothesis tests. These are sometimes known as statistical tests. This is where a p-value is used to decide whether or not to reject a statement called the null Hypothesis.

Learn about hypothesis tests

2. Types of Data

Next, you need to be familiar with different types of data. Variables can be classified as continuous, categorical, ordinal etc. Usually one variable is an outcome/ dependent variable while you may have several predictor/independent variables.

Learn about types of data

3. Some introductory tests

Finally, you will need to know about some specific analyses, what they are designed to do and how to interpret the results.

We strongly recommend that you begin by trying to understand the following statistical processes in approximately this order. These are not the only statistical techniques available but without an understanding of these processes, it’s usually difficult to understand other analyses.

T-tests (paired and unpaired)
Chi-Squared tests
ANOVA (one-way, repeated measures and two-way)
Simple Linear Regression
Multiple Regression

Learn about these processes

Choosing a test

You can use your knowledge of the topics above to begin identifying a test that will help you answer your research question. Ideally, we make a plan for the analysis before the data are collected.

Once you have decided on the test you would like to use, you need to check the assumptions. These are conditions which your data will need to meet if the hypothesis test is going to work. Tests which require the data to meet assumptions are called parametric tests. If you find that your data don’t meet the assumptions for the test you would like to perform, you may find there is a non-parametric test you can use instead.

What Test Tables

The charts below are designed to help you identify when to use some tests and their non-parametric alternatives.

Please note, these tables only show the most common tests.

Comparing Means (continuous variables)

	Paired or unpaired observations	Test	Assumptions not met
1 group	Comparing the mean to a fixed number	One-sample t-test	One-sample Wilcoxon signed-rank test
2 groups	Unpaired	Independent samples t-test	Mann-Whitney U test
2 groups	Paired	Paired t-test	Wilcoxon Signed rank test
3+ groups	Independent groups	One-way ANOVA	Kruskal-Wallis test
3+ groups	Repeated measurements	Repeated Measures ANOVA	Friedman test
2 grouping variables	Both grouping variables categorical, neither with repeated measures	Two-way ANOVA	No simple answer
2 grouping variables	Both grouping variables categorical, one with repeated measures	Mixed ANOVA	Friedman test

Comparing Count data (categorical variables)

Paired or unpaired observations	Test	Assumptions not met
Unpaired	Chi-squared test	Fisher’s exact test
Paired	McNemar’s test	No simple answer

Investigating Relationships

Variables	Dependent	Independent	Analysis
2 variables	continuous	continuous	Simple Linear Regression
2 or more	continuous	Two or more, any kind	Multiple Linear Regression
2 or more	binary	One or more, any kind	Binary Logistic Regression
2 or more	categorical	One or more, any kind	Logistic Regression

Links

These tables are our way of thinking through the different analyses available. There are many alternatives. Here are some links to websites we think are helpful:

Interactive statistical flowchart

Scribbr Webpage

Video from Dr. Nic’s Maths and Stats