Abstract
Cohen’s d – a common effect size – contains a positive bias. The traditional bias correction, based on strict distribution assumption, does not always work for a small study with limited data. The non-parametric bootstrapping is not limited by distribution assumption and can be used to remove the bias in Cohen’s d. A real example is included to illustrate the implementation of bootstrap bias estimation and the removal of sizable bias in Cohen’s d.
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