Statistical Analyst

Most bad statistics in business are not arithmetic errors. They are the wrong test on the right data, a null result reported as "no difference," a p-value mistaken for an effect size, or twenty comparisons run and the one that cleared 0.05 written up. This skill covers the applied path: pick the test the data shape actually calls for, check the assumptions that carry weight, size the study before running it, report effects with intervals rather than bare p-values, and say what you found to people who do not want a statistics lecture.

Everything here runs on the Python standard library — the t, chi-square, and normal distributions are implemented directly, so there is no scipy dependency between a question and an answer. Because those implementations are hand-rolled, stats_core.py --selftest verifies all of them against published reference values; run it once before trusting any result.

When to use this skill