Statistical Reporting

Statistical Reporting Best Practice

Test Selection Quick Reference

1. Comparing two groups (independent, normal): Independent t-test
2. Comparing two groups (independent, non-normal): Mann-Whitney U test
3. Comparing two groups (paired, normal): Paired t-test
4. Comparing two groups (paired, non-normal): Wilcoxon signed-rank test
5. Comparing 3+ groups (independent, normal): One-way ANOVA + post-hoc
6. Comparing 3+ groups (non-normal): Kruskal-Wallis test
7. Relationship between continuous variables: Pearson or Spearman correlation
8. Categorical outcomes: Chi-square or Fisher's exact test
9. Predicting continuous outcome: Linear regression
10. Predicting binary outcome: Logistic regression

Assumption Checking

1. Normality: Shapiro-Wilk test (n < 50) or visual Q-Q plots
2. Homogeneity of variance: Levene's test before t-tests and ANOVA
3. Independence: Verify study design ensures independent observations
4. Linearity: Scatter plots and residual plots for regression
5. Multicollinearity: VIF < 5 for multiple regression predictors
6. When assumptions are violated, use non-parametric alternatives or robust methods

APA Reporting Format

1. t-test: t(df) = X.XX, p = .XXX, d = X.XX
2. ANOVA: F(df_between, df_within) = X.XX, p = .XXX, eta-squared = .XX
3. Correlation: r(df) = .XX, p = .XXX [95% CI: .XX, .XX]
4. Chi-square: chi-square(df, N = XXX) = X.XX, p = .XXX
5. Regression: beta = X.XX, SE = X.XX, t = X.XX, p = .XXX
6. Always report exact p-values (not "p < .05") unless p < .001
7. Use leading zero for values that can exceed 1 (e.g., t = 0.50) but not for those bounded by 1 (e.g., p = .032, r = .45)