Glossary term
Effect Size
How big a difference or relationship actually is — separate from whether it is statistically significant.
Visual intuition
before
2 cases
x2
after
4 cases
Relative risk zooms in on the ratio
Effect size asks how big the change is: these illustrative crowds went from 2 cases to 4 — a ratio of 2 you can weigh, not just a yes/no verdict.
Example
In an illustrative trial, treated patients recover in 6 days on versus 8 days on placebo: the effect size is a 2-day reduction — recovery a quarter faster.
How It Works
A headache pill that shortens pain by 4 hours and one that shortens it by 4 minutes can both be '' with enough patients — significance answers 'is it real?', effect size answers 'how much?'. It comes in many forms: a difference in means, a , a . Small effects on huge samples produce impressive p-values and negligible benefits, so the effect size is usually the number worth asking for.
Concepts
Statistical Power
Statistical power is the chance that a study will detect an effect of a given size if that effect is real. Low-powered studies can miss real effects, while very large studies can detect effects too small to matter in practice.
Absolute vs Relative Risk
A relative change ('50% more', 'doubles your risk') hides how big the underlying risk actually is. A large relative increase on a tiny base rate is still tiny. Always ask for the absolute numbers — how many in 100 before and after — not just the percentage change.
Related Facts
Risk & Decisions
4 min · medium
Statistical power: a small study can miss a real effect
A study can report 'no clear effect' simply because it was too small and noisy to spot a real one.
Risk & Decisions
4 min · medium
A "50% higher risk" can still be almost nothing
Headlines shout '50% more likely!' — but 50% more of a small risk is still a small risk.