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Description: There is consensus that basing conclusions on confidence intervals for effect size estimates is generally superior to relying on null hypothesis significance testing. However, confidence intervals in psychology are typically very wide. One reason for this is a lack of easily applicable methods for planning studies to achieve sufficiently narrow confidence intervals. This paper presents tables and freely accessible R functions to facilitate planning studies for the desired accuracy in parameter estimation (i.e., Cohen’s d as an effect size). In addition, the importance of such accuracy is demonstrated using data from the Reproducability Project: Psychology. It is shown that the sampling distribution of Cohen’s d is very wide unless sample sizes are considerably larger than what is common in psychology studies. This means that effect size estimates can vary substantially from sample to sample, even with perfect replications. The Reproducability Project: Psychology replications’ confidence intervals for Cohen’s d have widths of around 1 standard deviation (the 95% confidence interval for the widths runs from 1 to 1.34 with a median width of 0.96). This means that replications of these replications are likely to find effect size point estimates that may vary substantially from the estimates from those original replications. The consequence is that researchers in psychology, but also the funders of research in psychology, will have to get used to conducting considerably larger studies if they are to build a strong evidence base.


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Knowing how effective an intervention, treatment, or manipulation is and increasing replication rates: accuracy in parameter estimation as a partial solution to the replication crisis

Although basing conclusions on confidence intervals for effect size estimates is preferred over relying on null hypothesis significance testing alone,...

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