Foundations & Assumptions
The Foundations series: check the assumptions every statistical test depends on — normality (Shapiro-Wilk, Q-Q plots) and homogeneity of variance — the tidy rstatix way…
Biostatistics with R
Biostatistics
Statistical tests in R, done the modern way — t-tests, ANOVA, correlation, normality and more — each lesson leads with the tidy rstatix workflow, shows the base-R equivalent.
Run the right statistical test in R — and trust the result because you can reproduce it. Every lesson leads with the modern, tidy rstatix workflow (pipe-friendly, ready for ggpubr plots), shows the equivalent base-R call (t.test(), aov(), cor.test()) so the classic syntax is never a mystery. No installs, no guessing.
Which test do I use?
- Compare two groups — Student / Welch / paired t-test (or the non-parametric Wilcoxon).
- Compare three or more groups — ANOVA (one-way, two-way, repeated-measures) or the non-parametric Kruskal-Wallis.
- Measure a relationship — correlation test (Pearson / Spearman / Kendall) and correlation matrices.
- Check the assumptions first — normality (Shapiro-Wilk, Q-Q) and homogeneity of variance.
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Note
This pillar covers correlation (the correlation test and the correlation matrix), assumption checks, the ANOVA family, two-group tests, categorical data, and inter-rater agreement. Each test cross-links the ggpubr way to plot and annotate it.
🟢 With an AI agent
Ask Prova “which test should I use to compare these groups, and how do I run it in R?” — it answers with rstatix code you can run on your own data, then helps you read the output. The runtime is the judge. Ask Prova →
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Citation
BibTeX citation:
@online{untitled,
author = {},
title = {Biostatistics with {R}},
url = {https://www.datanovia.com/learn/biostatistics/},
langid = {en}
}
For attribution, please cite this work as:
“Biostatistics with R.” n.d. https://www.datanovia.com/learn/biostatistics/.