data-stats-analysis

Hypothesis testing compares groups to determine if differences are statistically significant. This Skill performs t-tests, ANOVA, and non-parametric tests using SciPy and statsmodels, reporting test statistics and p-values to assess whether observed differences exceed random chance.

Correlation analysis measures relationships between variables. This Skill computes Pearson and Spearman correlations with p-values, helping you determine whether associations are statistically significant across your dataset.

Multiple testing corrections adjust p-values to reduce false positives when running many tests. This Skill applies FDR and Bonferroni methods to control error rates and ensure robust conclusions from your analysis.

Statistical tests assume data properties like normality. This Skill checks normality and other test assumptions using NumPy and statsmodels, generating confidence intervals and assessment reports to guide your test selection.

Yes. This Skill generates summaries and visualizations, exporting results in CSV or JSON formats so you can share findings, archive analyses, or integrate outputs into downstream reporting workflows.

Yes. This Skill supports varied sample sizes and distributions, offering non-parametric tests as alternatives when data violates normality assumptions, ensuring valid inference across research contexts.