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Statistical Test Selection Guide

This guide provides a decision tree for selecting appropriate statistical tests based on research questions, data types, and study designs.

Decision Tree for Test Selection

1. Comparing Groups

Two Independent Groups

• Continuous outcome, normally distributed: Independent samples t-test
• Continuous outcome, non-normal: Mann-Whitney U test (Wilcoxon rank-sum test)
• Binary outcome: Chi-square test or Fisher's exact test (if expected counts < 5)
• Ordinal outcome: Mann-Whitney U test

Two Paired/Dependent Groups

• Continuous outcome, normally distributed: Paired t-test
• Continuous outcome, non-normal: Wilcoxon signed-rank test
• Binary outcome: McNemar's test
• Ordinal outcome: Wilcoxon signed-rank test

Three or More Independent Groups

• Continuous outcome, normally distributed, equal variances: One-way ANOVA
• Continuous outcome, normally distributed, unequal variances: Welch's ANOVA
• Continuous outcome, non-normal: Kruskal-Wallis H test
• Binary/categorical outcome: Chi-square test
• Ordinal outcome: Kruskal-Wallis H test

Three or More Paired/Dependent Groups

• Continuous outcome, normally distributed: Repeated measures ANOVA
• Continuous outcome, non-normal: Friedman test
• Binary outcome: Cochran's Q test

Multiple Factors (Factorial Designs)

• Continuous outcome: Two-way ANOVA (or higher-way ANOVA)
• With covariates: ANCOVA
• Mixed within and between factors: Mixed ANOVA

2. Relationships Between Variables

Two Continuous Variables

• Linear relationship, bivariate normal: Pearson correlation
• Monotonic relationship or non-normal: Spearman rank correlation
• Rank-based data: Spearman or Kendall's tau

One Continuous Outcome, One or More Predictors

• Single continuous predictor: Simple linear regression
• Multiple continuous/categorical predictors: Multiple linear regression
• Categorical predictors: ANOVA/ANCOVA framework
• Non-linear relationships: Polynomial regression or generalized additive models (GAM)

Binary Outcome

• Single predictor: Logistic regression
• Multiple predictors: Multiple logistic regression
• Rare events: Exact logistic regression or Firth's method

Count Outcome

• Poisson-distributed: Poisson regression
• Overdispersed counts: Negative binomial regression
• Zero-inflated: Zero-inflated Poisson/negative binomial

Time-to-Event Outcome

• Comparing survival curves: Log-rank test
• Modeling with covariates: Cox proportional hazards regression
• Parametric survival models: Weibull, exponential, log-normal

3. Agreement and Reliability

Inter-Rater Reliability

• Categorical ratings, 2 raters: Cohen's kappa
• Categorical ratings, >2 raters: Fleiss' kappa or Krippendorff's alpha
• Continuous ratings: Intraclass correlation coefficient (ICC)

Test-Retest Reliability

• Continuous measurements: ICC or Pearson correlation
• Internal consistency: Cronbach's alpha

Agreement Between Methods

• Continuous measurements: Bland-Altman analysis
• Categorical classifications: Cohen's kappa

4. Categorical Data Analysis

Contingency Tables

• 2x2 table: Chi-square test or Fisher's exact test
• Larger than 2x2: Chi-square test
• Ordered categories: Cochran-Armitage trend test
• Paired categories: McNemar's test (2x2) or McNemar-Bowker test (larger)

5. Bayesian Alternatives

Any of the above tests can be performed using Bayesian methods: - Group comparisons: Bayesian t-test, Bayesian ANOVA - Correlations: Bayesian correlation - Regression: Bayesian linear/logistic regression

Advantages of Bayesian approaches: - Provides probability of hypotheses given data - Naturally incorporates prior information - Provides credible intervals instead of confidence intervals - No p-value interpretation issues

Key Considerations

Sample Size

• Small samples (n < 30): Consider non-parametric tests or exact methods
• Very large samples: Even small effects may be statistically significant; focus on effect sizes

Multiple Comparisons

• When conducting multiple tests, adjust for multiple comparisons using:
• Bonferroni correction (conservative)
• Holm-Bonferroni (less conservative)
• False Discovery Rate (FDR) control (Benjamini-Hochberg)
• Tukey HSD for post-hoc ANOVA comparisons

Missing Data

• Complete case analysis (listwise deletion)
• Multiple imputation
• Maximum likelihood methods
• Ensure missing data mechanism is understood (MCAR, MAR, MNAR)

Effect Sizes

• Always report effect sizes alongside p-values
• See effect_sizes_and_power.md for guidance

Study Design Considerations

• Randomized controlled trials: Standard parametric/non-parametric tests
• Observational studies: Consider confounding and use regression/matching
• Clustered/nested data: Use mixed-effects models or GEE
• Time series: Use time series methods (ARIMA, etc.)