Statistical Audit Overview Statistical Validity Score ↳ Normality & Homoscedasticity ↳ Effect Size Reporting ↳ CI Reporting ↳ Power & Sample Size ↳ Multiple Comparison ↳ Replication Type ↳ Missing Data Disclosure ↳ Outlier Pre-Registration Robustness & Sensitivity ↳ Sensitivity Analysis Engine ↳ Conclusion Sensitivity Profile ↳ Multiverse Analysis ↳ Outlier Sensitivity Report Estimation Methods ↳ Bootstrap CI ↳ Permutation Test ↳ Bayes Factor Supplement ↳ Assumption & Robustness Guard Test Configuration Guard ↳ Paired vs Unpaired Guard ↳ Multiple Comparison Enforce. ↳ One-Sided Test Lock ↳ Proportion OLS Prevention Experimental Design Guard ↳ Pseudoreplication Detection ↳ Bio vs Technical Replicate ↳ Batch / Plate Confounding ↳ Repeated Measures Suggestion Confounding & Independence ↳ Independence Formal Check ↳ Confounding Disclosure ↳ Covariate Selection Audit Regression & Modeling Guard ↳ Regression Diagnostics Guard ↳ Compositional Data Warning ↳ Sample Size Justification

Statistical Audit

Statistical errors in published research are rarely calculation mistakes — they are design and configuration mistakes. Wrong test for the data structure. Multiple comparisons without correction. Pseudoreplication treated as independent data. Statistical Audit is a collection of checks and guards that surfaces these risks before export.

What is Statistical Audit?

Statistical Audit is Licklider's pre-export diagnostic system. It aims to evaluate figures across several dimensions — including normality, effect size, confidence intervals, multiple comparison compliance, replication type, missing data disclosure, outlier criteria, and power justification. Not all dimensions are fully implemented; see individual pages for current coverage.

Beyond scoring, Statistical Audit operates through several guard layers that can block or warn at specific decision points: test configuration, experimental design, confounding, regression diagnostics, and robustness. Guards are tiered: some guards can affect export status, others surface risks with explanations, and others provide best-practice suggestions. Implementation depth varies by guard; individual pages document current product behavior and known limitations.

Statistical Validity Score

Robustness & Estimation

Guard Layers