domains / families / research / statistical-analysis

statistical-analysis

combined

Guided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or survey data, check statistical assumptions, compute required sample sizes, or write up results - even if they never name a specific test. Covers t-tests, ANOVA, chi-square, correlation, regression, non-parametric and Bayesian methods. For low-level model APIs, see the statsmodels and pymc skills. 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).

When to use

• Conducting statistical hypothesis tests (t-tests, ANOVA, chi-square, non-parametric)
• Performing regression or correlation analyses
• Running Bayesian statistical analyses
• Checking statistical assumptions and diagnostics
• Calculating effect sizes and conducting power analyses
• Reporting statistical results in APA format
• Analyzing experimental or observational data for research
• Choose an appropriate statistical test (e.g., t-test vs. ANOVA vs. non-parametric vs. Bayesian) based on study design and variable types.

When not to use

• Do not proceed when required input files, identifiers, parameters, or context are missing — ask the user to provide them first.
• Do not assume capabilities beyond this skill's declared scope when the user requests external operations or inferences.
• Do not proceed without user confirmation when overwriting existing results, executing high-cost batch operations, or expanding task scope.

Skill page

Conflict

The K-Dense-AI copy is the most complete and current procedure, but it contains specific, version-dependent constraints (e.g., Pingouin 0.7.0 API changes, PyMC 6.3.2 xarray output, and a specific `uv` installation workflow) that the other copies lack. The other copies (zLanqing, foryourhealth111, aipoch) provide a more generic, version-agnostic workflow and include a full end-to-end Python example (aipoch) that the K-Dense copy omits. Crowning the K-Dense copy would hide the generic alternative and the runnable example, while crowning the others would hide the critical version-specific warnings.