📊 Two-Sample T-Test

Compare 2 independent groups

📈 One-Way ANOVA

Compare 3+ independent groups

🎯 Two Proportions

Compare binary outcomes

🔄 Repeated Measures ANOVA

Within-subjects over time

📊 Chi-Square Test

Categorical associations

🔗 Paired T-Test

Before/after comparisons

📍 One-Sample T-Test

Compare mean to fixed value

🎯 One-Sample Proportion

Compare rate to fixed value

🔄 McNemar's Test

Paired categorical data

📊 Mann-Whitney U Test

Nonparametric 2-group comparison

🔬 Mixed Effects Model

Hierarchical/clustered data

⏱️ Survival Analysis

Log-rank test, hazard ratios

📉 Logistic Regression

Binary outcome models

🔗 Correlation Analysis

Pearson/Spearman correlation

⚖️ Non-Inferiority/Equivalence

Show not worse or equivalent

📈 Multiple Linear Regression

Multiple continuous predictors

📊 Two-Way ANOVA

Two factorial variables

🔬 ANCOVA

Adjust for covariates

⏱️ Cox Proportional Hazards

Survival with covariates

📊 Poisson Regression

Count outcome models

🔗 GEE Model

Correlated/longitudinal data

🎲 Simulation-Based Power

Complex designs via simr

🏥 Cluster Randomized Trial

Cluster-level randomization

📶 Stepped-Wedge CRT

Sequential cluster crossover

🔄 Crossover Design

Within-subject treatment periods

🔀 Factorial Design

2×2 or higher factorial trials

📚 Meta-Analysis Power

Power for meta-analysis studies

🔄 Sample Size Re-estimation

Adaptive trial designs

🎯 Binary Outcome Prediction

Riley criteria for logistic models

⏱️ Survival Outcome Prediction

Riley criteria for Cox models

📊 Continuous Outcome Prediction

Riley criteria for linear models

✓ External Validation Study

Sample size for model validation

🤖 High-Dim ML/Penalized Models

LASSO, Ridge, Random Forest, XGBoost