Introduction
Statistical Analysis selects methods from the research question, variable types, sample structure, and distribution. It can cover t-tests, ANOVA, chi-square tests, correlation, regression, and common Bayesian analyses. The workflow includes data inspection, assumption diagnostics, estimation or testing, effect sizes, confidence intervals, power, standardized reporting, and explicit limits on causal interpretation.
Use Cases
Use it for controlled experiments, surveys, group comparisons, variable relationships, predictive models, and result review. It can choose a test from the study design or check whether independence, normality, equal variance, multiple comparisons, and sample size were handled properly.
Template
Provide the research question, hypotheses, data, variable definitions, sampling, groups, and design. State missing-data rules, alpha level, and prior analyses. Add frequentist or Bayesian preference, target effect size, power needs, figures, reporting style, and interpretation boundaries.
