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Making multiple comparisons easy: a decision tree and visual statistics guide for data with more than two groups.
Cody J Gurski1, Bonnie N Dittel1,2
1Versiti Blood Research Institute, Milwaukee, WI, United States.
This guide explains analysis of variance (ANOVA) and multiple comparison tests for scientists. It offers practical advice on selecting, interpreting, and presenting data from experiments with multiple groups.
Area of Science:
- Biostatistics
- Experimental Design
Background:
- Analysis of Variance (ANOVA) is commonly taught but often poorly explained.
- Understanding principles and follow-up steps for ANOVA is crucial for accurate data analysis.
Purpose of the Study:
- Provide a practical guide to selecting appropriate multiple comparison tests after ANOVA.
- Aid in the accurate interpretation and presentation of experimental data.
Main Methods:
- Overview of commonly used multiple comparison tests.
- Discussion of data interpretation and presentation strategies.
Main Results:
- Highlights practical approaches for post-ANOVA analysis.
- Identifies statistical tools available in software like GraphPad Prism.
Conclusions:
- Empowers scientists to confidently apply ANOVA and multiple comparison tests.
- Facilitates accurate and clear reporting of experimental findings.
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