Effect Size Calculator & Interpretation Guide

Pro v1.0.0 1 view

Calculate and interpret effect sizes (Cohen's d, eta-squared, odds ratios, correlations) with context-specific guidance. Distinguish statistical significance from practical importance and convert metrics for meta-analysis.

What You Get

Transform statistical results into actionable insights by distinguishing significance from practical importance

The Problem

A statistically significant result (p < 0.05) doesn't tell you whether the effect is practically meaningful. With large samples, even trivial effects become significant; with small samples, meaningful effects may not reach significance. Additionally, conducting meta-analyses across studies requires converting between different effect size metrics (Cohen's d, correlations, odds ratios, eta-squared), each with its own assumptions and limitations.

The Solution

This skill helps researchers, analysts, and product teams move beyond p-values to understand the actual magnitude and practical importance of their findings. It calculates effect sizes (Cohen's d, Hedge's g, eta-squared, Pearson's r, odds ratios), interprets magnitude in domain context rather than using arbitrary Cohen benchmarks, distinguishes statistical from practical significance with confidence intervals, converts between effect size metrics for meta-analysis with documented limitations, and generates APA-formatted reporting recommendations.

How It Works

  1. 1 Identify study context including research domain, study design, and available statistics
  2. 2 Calculate appropriate effect size metrics (Cohen's d, Hedge's g, eta-squared, correlations, odds ratios)
  3. 3 Interpret magnitude by comparing to domain-specific distributions and translating to real-world consequences
  4. 4 Distinguish statistical from practical significance using confidence intervals and ROI frameworks
  5. 5 Convert effect sizes between metrics for meta-analysis with documented assumptions and limitations
  6. 6 Generate publication-ready reports in APA format with context-specific interpretation

What You'll Need

  • Summary statistics (means, SDs, sample sizes) or raw data from study
  • Type of comparison or relationship being analyzed
  • Statistical test results if available (t-test, ANOVA, regression, etc.)
  • Domain or field of study for context-specific interpretation
  • Multiple studies with different metrics if conducting meta-analysis conversions

Get This Skill

Requires Pro subscription ($9/month)

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Examples

A/B Test: Large Sample Statistical Trap

Demonstrates the classic 'large sample trap' where statistical significance (p=0.008) masks a very small effect size (Cohen's h=0.018). Provides business impact scenarios at different traffic levels and a decision framework for implementation.

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Education Study: Flipped Classroom Intervention

Analyzes a teaching methods comparison where the p-value narrowly misses significance (p=0.052) but the effect size (d=0.40) is educationally meaningful. Demonstrates interpreting non-significant results with substantial effect sizes and comparing to domain benchmarks.

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Meta-Analysis Effect Size Conversions

Converts effect sizes from three studies using different metrics (correlation r, odds ratio, F-statistic) to Cohen's d for meta-analysis. Includes conversion formulas, assumptions, and ready-to-use methods text.

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