statistical-analysis

Reporting statistical analysis results in APA format requires structuring test statistics, degrees of freedom, p-values, and effect sizes into standardized sentences. This skill provides structured guidance to generate APA-compliant reporting outputs for academic research.

Choosing a statistical test for comparing groups depends on your data's normality and variance homogeneity. This skill offers guidance on test selection between t-tests, ANOVA, and nonparametric alternatives for two-group and multi-group comparisons.

Calculating effect sizes like Cohen's d and partial eta-squared in Python involves using specialized libraries such as pingouin. This skill provides Python code examples for computing and reporting these effect sizes for your analyses.

Checking normality and homogeneity of variance before running an ANOVA requires specific statistical tests to validate assumptions. This skill includes normality and variance checks with example code snippets using scipy.stats and pingouin.

Running regression analyses and reporting R-squared in APA style is supported for evaluating predictor relationships with continuous outcomes. This skill guides test execution and formatted reporting using statsmodels and other Python libraries.

Common pitfalls when planning hypothesis testing for academic research include ignoring assumption checks and misreporting effect sizes. This skill provides a practical checklist to ensure robust analyses and avoid common errors.