recommend_test
Read-onlyIdempotent
Find the right statistical test by describing your data's outcome type, group count, and pairing. Receive a recommendation, reason, and alternative tests to consider.
Instructions
Not sure which rigor tool fits your question? Answer a few characteristics of the data and get back which tool to call, why, what to call instead if this test's assumptions look shaky, and what to run alongside it (an effect size, a power calculation, a natural follow-up). Every test in this package already documents this guidance in its own docstring for the sibling comparisons it knows about -- this tool exists so you don't have to have already read every other tool's docstring to find the one relevant cross-reference. Pure decision logic, no statistics computed here.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| paired | No | for n_groups=2 (continuous/rank_or_ordinal/proportion): were the same subjects measured twice, rather than two independent groups? | |
| n_groups | No | 1 = one sample vs. a hypothesized value; 2 = two groups/conditions; 3+ = three or more groups. Ignored when testing_association=true. | |
| outcome_type | Yes | what kind of thing is being compared/measured: "continuous" (means), "proportion" (rates), "count_or_category" (category counts / contingency tables), or "rank_or_ordinal" (ordinal data -- always routed to a rank-based test) | |
| small_or_skewed | No | is the sample small, visibly skewed, or outlier-heavy? nudges toward the non-parametric alternative | |
| testing_association | No | this is "does x relate to/predict y" for two continuous or ranked variables, not a group comparison -- routes to correlation/regression instead | |
| two_categorical_variables | No | for outcome_type="count_or_category": testing association between two categorical variables (a contingency table) rather than counts against an expected distribution? |