Use Cases Overview Group Comparison & ANOVA ↳ Two-Group Comparison ↳ One-Way ANOVA ↳ Two-Way ANOVA ↳ Repeated Measures Regression & Curve Fitting ↳ Linear Regression ↳ Logistic Regression & AUC ↳ Dose-Response & IC50 ↳ Mixed Models (GLMM) Survival Analysis ↳ Kaplan-Meier Workflow ↳ Cox Regression Workflow Normality & Power ↳ Pre-Study Power Calculation ↳ Normality Testing Categorical & Association ↳ Chi-Square Test ↳ Fisher's Exact Test ↳ Odds Ratio & Risk Life Sciences Applications ↳ Flow Cytometry ↳ Gene Expression / Omics ↳ Animal Study Workflow ↳ Clinical Trial Endpoints

Categorical & Association

When outcomes are categorical — binary, nominal, or ordinal — standard parametric tests do not apply. Licklider guides researchers through chi-square, Fisher's exact test, and odds ratio calculations, enforcing the assumptions (expected cell frequencies) and effect size reporting that journals require for categorical analyses.

Section covers: Chi-square test for independence · Goodness-of-fit · Fisher's exact test (2×2 and higher) · Odds Ratio (OR) · Relative Risk (RR) · Absolute Risk Reduction (ARR) · Number Needed to Treat (NNT) · Cramér's V · Phi coefficient · Expected cell frequency check

When to Use This Section

Use Categorical & Association workflows when:

  • Your outcome variable is categorical (binary, nominal, or ordinal with few levels)
  • You want to test whether two categorical variables are independent (chi-square) or associated (odds ratio)
  • Sample sizes are small and expected cell counts fall below 5 (Fisher's exact test)
  • You are reporting risk ratios, odds ratios, or absolute risk differences from a 2×2 table (clinical research)

Available Scenarios

Test Selection Guide

  • Large sample (expected cell ≥5 in all cells): Chi-square test for independence
  • Small sample (any expected cell <5): Fisher's exact test (exact p-value, no chi-square approximation)
  • 2×2 prospective / RCT data: Relative Risk (RR) is preferred over Odds Ratio (OR) when the outcome is common (>10%) — Licklider enforces this distinction
  • 2×2 case-control data: OR is the appropriate measure (RR cannot be estimated from case-control designs)
  • Effect size for chi-square: Cramér's V (2×2: equivalent to phi; larger tables: Cramér's V with bias correction)