statistical-power

Adaptive Bayesian Clinical Trial

Statistical power analyses in the browser

Power and Sample Size Calculation for the Cochran-Mantel-Haenszel Chi-Squared Test

PRISME Power Calculator

Code for "Adaptive Selection of the Optimal Strategy to Improve Precision and Power in Randomized Trials"

This incomplete repository is used to facilitate the consultation of individual files in this project. Only files smaller than 100 MB are available here. The complete project is available at https://doi.org/10.17605/OSF.IO/GT5UF.

Eval suites that tell you when they've gone blind: coverage, detection power and judge depth for LLM agent evaluation.

Identifying and avoiding common misinterpretations in using statistics

Applied statistics casebook: A/B-testing business cases (ROI, MDE, Bonferroni, selection bias) with decks, plus a 12-part statistical inference workbook

How many runs before your eval means anything? Reliability statistics for stochastic evals: audit miss rates, exact intervals, runs-needed.

Simulation studies of power and Type I error of mass univariate statistics for ERP data

Could these celiac trials have detected their drugs? TAK-101 prevented 71% of gluten injury and was written up as a failure. At 13 patients per arm it needed 95%. Endpoint noise is not constant: SD = 0.40 + 0.30 × injury.

Size your early-stopping window by statistical power instead of by habit

Companion Code for the Medium Article on top Python Data Science Interview Questions.

Reproducible statistical-power simulation suite for adaptive studies with an embedded active-inference agent: multiple-testing corrections, sequential e-processes, and action-loop operating characteristics, using real pymdp inference.

This module allows running Power Analyses in JASP

Measures LLM brand-recommendation variance and the minimum change a weekly tracker can actually detect

This incomplete repository is used to facilitate the consultation of individual files in this project. Only files smaller than 100 MB are available here. The complete project is available at http://doi.org/10.17605/OSF.IO/UERYQ.

Visualise the output from allFit(), to look at the parameters for a set of predictors across a set of optimizers (e.g., bobyqa, Nelder-Mead, etc.)

Monte Carlo simulation study comparing ANOVA, Welch's ANOVA, and Kruskal–Wallis under small samples