Use this guide for first-pass decisions when choosing a statistical comparison for a lab experiment, especially when you need to separate paired designs, independent groups, or multi-group comparisons.
Why this decision is easy to get wrong
Many lab analyses fail before software is opened. The wrong decision often happens when experimental design is not mapped clearly: repeated measurements are treated as independent samples, multiple endpoints are tested without correction, or assumptions are ignored because the graph "looks normal enough."
Common input mistakes
- Wrong pairing of repeated measures with independent tests.
- Omitting test assumptions such as normality or variance equality.
- Using one-tailed testing without a pre-specified directional hypothesis.
- Treating technical replicates as if they were biological replicates.
Interpretation pitfalls
- A p-value is not an effect size.
- Multiple testing increases false positives and should be corrected.
- Small sample sizes can make assumption checks unstable.
- A statistically significant result may still be biologically trivial.
Recommended tools
- R with transparent scripts for reproducible workflows.
- GraphPad Prism for quick GUI-based analysis in small teams.
- Python SciPy for code-driven pipelines and automation.
Practical review checklist
- Write down the experimental unit before choosing any test.
- Separate technical replicates from biological replicates.
- Check whether groups are paired, repeated, or fully independent.
- Decide whether you are comparing two groups or several groups.
- Record assumption checks and any multiple-testing correction.
Alternative references
- Official university or public statistics method pages
- Introductory biostatistics textbooks
- Internal lab SOPs for common assay readouts
Disclaimer
This page is only a starting point. Complex designs, batch effects, mixed models, and repeated measures across time usually require a more specific analysis plan.