Effektestimater

Effektestimater is a term used in statistics to describe a class of estimators designed to estimate the effect of a treatment or intervention by combining a hypothesis test with estimation. The aim is to produce an estimator with favorable mean squared error properties through data-driven shrinkage toward a null value when evidence against the null is weak.

Construction: Start with a conventional estimator of the effect, such as a regression coefficient. Conduct a test of a null hypothesis that the true effect equals a prespecified value (often zero). If the null is not rejected at a chosen alpha, adjust or shrink the estimator toward the null; if rejected, report the conventional estimator. The amount of shrinkage depends on the test statistic and the chosen decision rule.

Properties: Effektestimater blends estimation and testing, potentially reducing variance at the cost of introducing bias when the null holds. Its performance is data- and model-dependent and relies on correct specification and calibration of the test.

Relation to related concepts: It is related to the broader idea of test estimators, shrinkage estimators, and empirical Bayes methods. It shares the motivation of reducing risk of overconfident estimates in small samples.

Applications: Used in fields such as clinical trials, social science, and economics where researchers are interested in both testing hypotheses about effects and providing precise effect estimates.

Limitations: The method requires a predefined null value and careful choice of alpha; the resulting estimator can be biased if the null is misspecified; practitioners must be wary of data-dredging and multiple testing issues.

See also: testimator, shrinkage estimator, empirical Bayes, causal inference, effect size estimation.