Individual skill
statistical-analysis
Guided statistical analysis for test selection, assumption checks, power analysis, and APA-style reporting. Use when you need to choose an appropriate statistical test for your data and produce publication-ready results (including effect sizes and diagnostics).
scientific-skills/Data Analysis/statistical-analysis/SKILL.md
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- bioinformatics Repository topic
- computational-biology Repository topic
- data-analysis Repository topic
- ai Repository topic
- biology Repository topic
- cancer-genomics Repository topic
- data-visualization Repository topic
- differential-expression Repository topic
- genomics Repository topic
- medical Repository topic
- data Derived category
- testing Derived category
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POLISH_CHANGELOG.mddoc · 603 bytesSKILL.mdcore · 11,282 bytesSKILL.mdcore · 11,282 byteseval_report_statistical-analysis_result.jsonasset · 10,976 bytesreferences/assumptions_and_diagnostics.mddoc · 11,801 bytesreferences/bayesian_statistics.mddoc · 19,171 bytesreferences/effect_sizes_and_power.mddoc · 16,691 bytesreferences/reporting_standards.mddoc · 20,751 bytesscripts/assumption_checks.pyruns · 16,398 bytes
Read excerpt
> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)
## When to Use
Use this skill when you need to:
1. Choose an appropriate statistical test (e.g., t-test vs. ANOVA vs. non-parametric vs. Bayesian) based on study design and variable types.
2. Validate assumptions before inference (normality, homoscedasticity, linearity, outliers) and decide on remedies when assumptions fail.
3. Run common inferential analyses (hypothesis tests, correlation, regression) and interpret results with effect sizes and uncertainty.
4. Plan studies with a priori power analysis (sample size planning) or run sensitivity analysis after data collection.
5. Write results in APA style with complete reporting elements (test statistic, df, p, effect size, CI, assumption checks).
> For programming model-specific workflows (especially regression variants and custom diagnostics), prefer **statsmodels** directly; this skill focuses on *guided selection, checks, interpretation, and reporting*.
