Statistics

objective

Apply robust statistical inference to quantify uncertainty, avoid false discoveries, and validate model assumptions.

workflow

1. define hypotheses, sampling assumptions, and test-selection criteria.
2. estimate parameters and uncertainty intervals with robust methods.
3. run assumption validation for distribution, dependence, and variance stability.
4. evaluate multiple-testing and false-discovery risk in signal screening.
5. publish inference only when validation and robustness checks pass.

required validation

• p-value calibration and type-i/type-ii error balance.
• confidence-interval coverage and estimator stability.
• residual normality, autocorrelation, and heteroskedasticity validation.
• multiple-testing adjustment impact on selected signals.
• outlier and leverage-point sensitivity analysis.