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Rigor for statistical inference (is the difference real): hypothesis tests, power, multiple comparisons, effect size over p-value.
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SOC 職業分類に基づく
Establish a dumb baseline and an honest validation harness before any real model, so every later number means something.
Rigor for causal questions and A/B tests (the effect of acting on X): confounding, post-treatment bias, valid control groups.
Rigor for descriptive and diagnostic analytics (what happened and why): denominators, grain, and confounded slices, not model leakage.
EDA kickoff for a fresh dataset: fixed opening scaffold (goal, imports, load, sanity), then chapters derived from the data; scan leakage, land a baseline.
Audit a dataset or pipeline for the five leakages that inflate a metric: target, preprocessing, temporal, group, and sampling.
Final modeling once the baseline and feature set are locked: tune against validation, audit overfitting, touch the test set once, justify the complexity.
| name | lemma-inference |
| description | Rigor for statistical inference (is the difference real): hypothesis tests, power, multiple comparisons, effect size over p-value. |
| homepage | https://github.com/tkpratardan/lemma |
| license | MIT |
Report the estimand, effect estimate, uncertainty interval, population and denominator, method and assumptions, practical interpretation, and any power or multiplicity limitation.
Do not equate significance with importance or causality, treat dependent observations as independent, or report p-values without effect sizes.
For method selection and power detail, read references/deep-guide.md.
