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- Stats Tests
- Independent-Samples t-Test
- Paired-Samples t-Test
- Mann Whitney U-Test
- Wilcoxon Signed-Ranks Test
- One-Way Between-Subjects ANOVA
- One-Way Repeated-Measures ANOVA
- Two-Way Mixed ANOVA
- Kruskal Wallis H-Test
- Friedman's ANOVA
- Pearson's 'r' Correlation
- Spearman's Rho Correlation
- Simple Linear Regression
- Multiple Linear Regression
- Chi-Squared (χ2) Test of Association
- Shapiro-Wilk Test of Normality

- Terminology

We have provided some guides to help you with carrying out a number of commonly used statistical tests.

This list of statistical tests is not exhaustive.

We show you the process to carry out the test in common statistical packages, examples of the output it will produce, and an example of how you could report it using the correct APA style.

If there is a test you would like to do see here, please email us your suggestion.

*between-subjects design*. If we have one participant group and we measure them multiple times, we can test if there is a difference in the repeated measurement and therefore, we have a *repeated-measures design*.

In some subjects, like Psychology, a *repeated-measures design is called a within-subjects design.*

Data may be described as as **parametric**, for data to be parametric it must possess four characterises sometimes called the Parametric Assumptions. Data is parametric if it is normally distributed, homogenous, interval level (or higher) and independent.

Parametric tests, such as an Independent Samples t-Test, should only be used on parametric data, is your data is non-parametric you should a non-parametric version of the test, such as the Mann-Whitney U-Test

For more information on determining if your data is parametric or not, please our guide:

We can also directly test if your data is normally distributed by conducting a normality test.

- Last Updated: Dec 1, 2022 1:56 PM
- URL: https://guides.library.lincoln.ac.uk/mash
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