smalleffect

Smalleffect is a term used in statistics and social sciences to describe an effect that is statistically detectable but of small magnitude. The label is typically applied to results in which the average difference between groups or conditions is modest, yet consistent enough to achieve statistical significance.

Origin and usage: The term arises in discussions of effect size and is common in meta-analytic findings, program evaluation, and policy analysis. It serves to distinguish the presence of an effect from its practical importance and to acknowledge that small effects can be meaningful when observed across large samples or many contexts.

Measurement: Smalleffect is quantified using standard effect-size metrics such as Cohen's d, Hedges g, Pearson's r, or odds ratios. By convention, Cohen's d around 0.2 is often labeled small, with 0.5 as medium and 0.8 as large; however, interpretation is context-dependent.

Interpretation: Small effects may accumulate over time or across multiple subgroups, leading to noticeable population-level impacts. They can inform policy and program design when the intervention is inexpensive, scalable, or targeted to many individuals.

Limitations and critique: Detecting small effects requires adequate sample size and power. Small effects are more susceptible to sampling error and publication bias, and their practical significance depends on context, cost, and feasibility. Researchers are advised to consider confidence intervals, heterogeneity, and cumulative effects when reporting smalleffects.

See also: effect size, practical significance, meta-analysis, statistical significance.