foundations-statistical-inference
Statistical Inference Foundations
Turn observations into appropriately bounded claims. A numerical estimate is incomplete without its population, estimand, sampling mechanism, independent unit, uncertainty, and assumptions.
Triggers: sampling uncertainty, confidence or credible intervals, effect-size precision, statistical power, multiple comparisons, optional stopping, Bayesian inference, predictive calibration, or conformal prediction.
Use measurement theory for whether the instrument measures the intended construct, causal inference for identification of intervention effects, and decision theory for choosing actions given uncertainty. This skill owns inference contracts; applied evaluation workflows remain with ai-evals. Do not create causal claims from statistical significance.
Quick Reference
| Need | Load |
|---|---|
| Population, estimator, confidence or credible interval | Sampling and estimation |
| Precision, selection, repeated looks | Design and sequential inference |
| Forecast uncertainty or conformal threshold | Predictive calibration |