Recommend the correct statistical test for a study design.

Use this to answer "what statistical test should I use?". Describe the design: outcome_type (continuous/ordinal/nominal/count), design (one_sample = compare one group to a value; independent = compare separate groups; paired = same subjects over time/conditions; correlation = relationship between two variables; association = two categorical variables), how many groups, whether the outcome is ~normal, and whether group variances are equal. Returns the test, why, assumptions, SPSS path, R code, an APA reporting template, and fallbacks if assumptions fail.

List the assumptions of a specific test, how to check each, and what to do if violated.

Pass a test_id from recommend_test / list_supported_tests (e.g. 'independent_t', 'one_way_anova', 'pearson', 'chi_square_independence').

Interpret a p-value correctly and produce a defensible, APA-style conclusion.

Guards against the classic mistakes: a non-significant result does NOT prove the null, and statistical significance is not practical importance (report effect size + CI).

A priori power analysis: the required sample size for a target power.

effect_size is Cohen's d for two_means/paired_means, Cohen's h for two_proportions, and the correlation r for correlation. Uses a normal approximation — treat the result as a close lower bound and confirm exact numbers in G*Power for t-based tests.

How to decide and report normality correctly — the #1 thing students get wrong.

List every test this advisor knows, with its SPSS menu path.