Effect Size Calculation

About

This skill empowers researchers and data scientists to move beyond binary p-value testing by quantifying the magnitude of their results through standardized effect size measures. It provides specialized guidance for calculating Cohen's d for group comparisons, Pearson's r for correlations, Eta-squared for ANOVA variance, and Odds Ratios for binary outcomes. By integrating confidence interval reporting and offering clear interpretation thresholds, it ensures that research findings are presented with the necessary context for meta-analyses, grant writing, and robust sample size planning.

Key Features

• 2 GitHub stars
• Confidence interval integration for reporting precision
• Support for Cohen's d, Pearson's r, Eta-squared, and Odds Ratios
• Integration patterns for power analysis and study planning
• Guidance for ANOVA variance and binary outcome analysis
• Standardized interpretation benchmarks for effect magnitudes

Use Cases

• Planning study power and justifying sample sizes during grant writing
• Reporting research results to demonstrate practical significance in academic publications
• Conducting meta-analyses to combine findings across multiple independent studies