Effect size
Definition
Effect size is a quantitative measure of the magnitude of a relationship or difference observed in research, distinct from statistical significance, which captures only whether the effect is unlikely to be zero. Effect size is the bridge between statistics and substantive meaning — what tells you whether a finding actually matters.
Why it matters
Effect size matters because statistical significance tells you only whether an effect is unlikely to be zero; it tells you nothing about whether the effect is meaningful. A treatment that reduces dementia risk by 0.5% can be highly statistically significant in a large study and clinically negligible. A treatment that reduces risk by 20% can be statistically inconclusive in a small study and clinically transformative. Effect size is the bridge between statistics and substance — the metric that lets you ask "how much does this actually matter?"
Origin and lineage
The technical concept of effect size was substantially formalized by Jacob Cohen in his 1969 textbook Statistical Power Analysis for the Behavioral Sciences, which proposed standardized effect size measures (Cohen's d, r, f) and benchmarks for interpreting them as small (d = 0.2), medium (d = 0.5), or large (d = 0.8). Cohen's framework was foundational, though contemporary research has emphasized that "small" and "large" depend heavily on context: an effect size of d = 0.2 in a public health intervention applied to billions of people may be enormously consequential, while d = 0.5 in a tightly-controlled lab study may be unremarkable.
Research evidence
The replication crisis in psychology and biomedicine, beginning in earnest with the Open Science Collaboration's 2015 reproduction project, brought renewed attention to effect sizes. Many high-profile findings turned out to have inflated effect sizes in the original studies and shrunk substantially on replication. The contemporary statistical reform movement — advocated by figures like Geoff Cumming, Daniel Lakens, and the Society for the Improvement of Psychological Science — has emphasized reporting effect sizes with confidence intervals, registering studies in advance, and treating effect size estimation rather than statistical significance as the primary inferential goal.
Common misconceptions
Statistical significance is not effect size. A p-value of 0.001 does not mean a large effect; it means the effect is unlikely to be zero. Effect size also is not a single metric; different effect size measures (Cohen's d, correlation r, odds ratio, risk ratio) capture different aspects and are appropriate for different research designs. "Small" and "large" effect sizes are context-dependent: small effect sizes can have large public health impact when applied at population scale, and large effect sizes in small clinical trials can fail to replicate.
How LifeByLogic uses it
Effect sizes underlie the published research on which our tools rest. The 2024 Lancet Commission's population attributable fractions are effect size measures at the population scale; the meta-analytic correlations in the turnover literature (Steel & Ovalle 1984, r = 0.50) are effect sizes at the individual scale. Methodology pages report these effect sizes explicitly to help users gauge the strength of the underlying evidence.