The Big Picture
Effect size tells us how big the difference is between two groups—not just whether the difference is statistically significant.
One of the most common measures in t-tests is Cohen’s d. It measures the magnitude of the difference expressed in standard deviation units.
"How much do the two groups differ, regardless of sample size?"
Practical Importance
Even a tiny difference can be statistically significant with a huge sample size. Effect size helps you judge if that difference actually matters in the real world.
Interactive Lab: Visualizing Cohen's d
Drag the slider to change the difference between group means and watch the effect size change.
🧮 The Formulas
1 Independent Samples t-test
- X̄₁, X̄₂: Group means
- spooled: Pooled standard deviation
2 Paired Samples
- D̄: Mean of the differences
- sD: Standard deviation of differences
📊 Interpreting the Results
These rules of thumb are widely used, but remember: context is key! In education or medicine, even a small effect (0.2) can change lives.
Example Scenario
- Group A Mean 75
- Group B Mean 70
- Pooled SD 10
Result: Medium Effect Size
✅ Reporting Best Practices
Report It
Always report Cohen's d after every t-test in your thesis or paper.
Contextualize
Include it in tables alongside means, SDs, and p-values for a complete picture.
Calculation
SPSS doesn't always do it automatically. Use Excel, online calculators, or plugins.