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statistical-reporting

statistical-reporting

github.com/aiming-labData & databases14.8k40 installsUpdated 10w ago
Repository

About statistical-reporting

A statistical analysis guide for researchers that helps select tests, check assumptions, and report results in APA format. It also emphasizes effect sizes, confidence intervals, and avoiding common interpretation errors.

Key features

  1. 01Choose tests for data and study design
  2. 02Check normality and variance assumptions
  3. 03Use APA result-reporting formats
  4. 04Report effect sizes and confidence intervals
  5. 05Avoid common statistical pitfalls

Use cases

  1. 01Select tests for group comparisons
  2. 02Check assumptions for regression analyses
  3. 03Standardize statistical reporting in papers

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/aiming-lab/autoresearchclaw --skill statistical-reporting

After installing, type/statistical-reportingin Claude Code to confirm it loaded.

Details

From the project README

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)
Effect Sizes
  1. ALWAYS report effect sizes alongside p-values
  2. Cohen's d for group comparisons: small = 0.2, medium = 0.5, large = 0.8
  3. Eta-squared for ANOVA: small = .01, medium = .06, large = .14
  4. R-squared for regression: report adjusted R-squared for multiple predictors
  5. Odds ratios for logistic regression with 95% confidence intervals
  6. Distinguish statistical significance from practical significance
Common Mistakes to Avoid
  1. Never say "the results were not significant, therefore there is no effect"
  2. Do not confuse correlation with causation in observational data
  3. Apply multiple comparison corrections (Bonferroni, FDR) when running many tests
  4. Report confidence intervals, not just point estimates
  5. State whether tests are one-tailed or two-tailed and justify the choice

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