Practical application of a minimal important percent difference formulation of Cohen’s d

ABSTRACT

Because Null Hypothesis Significance Tests (NHST) can be misleading particularly when sample sizes are large, reporting effect sizes is recommended. Effect sizes such as Cohen’s d, however, are abstract statistics and not relevant to decision-making. This article introduced an alternative formulation of Cohen’s d based on a “minimal important percent difference” (MIPD) between groups that has an intuitive, practical meaning. Cohen’s d was calculated based on detecting MIPDs of 5% and 10% differences in means. Power analyses were used to determine the number of responses needed to detect differences in means. Power-probability sample size requirements were examined for an 80% and a 50% chance of accepting a difference of MIPD at p < .05. We proposed a four-step process to show how both effect size and NHST jointly contribute to decision-making. To demonstrate the process, data were obtained for 6 years of annual waterfowl surveys in Illinois (N = 17,265, average response rate = 41%).

KEYWORDS:

• Cohen’s d
• minimal important percent difference
• power analysis
• wildlife surveys

Disclosure Statement

No potential conflict of interest was reported by the author(s).