Welcome to Kristoffer Magnusson's blog about

R, STATISTICS, PSYCHOLOGY, OPEN SCIENCE, DATA VISUALIZATION

Expected overestimation of Cohen’s d under publication bias

In this post I will use the theoretical and empirical sampling distribution of Cohen’s d to show the expected overestimation due to selective publishing. I will look at the overestimation for various sample sizes when the population effect is 0, 0.2, 0.5 and 0.8. The conclusion is that you should be weary of effect sizes from small samples, and that the issue is rather with type M (magnitude) errors than type I errors. At least is clinical psychology the pervasive problem is overestimation of effects and not falsely rejecting null hypothesis.

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Are the Current Criteria for Empirically Supported Treatments Too Lenient?

The practice of classifying treatments as empirically supported has been widely debated for a long time. In this post I write about a recent article that raises several concerns and suggestions regarding the current use of EST criteria—which can be summarized as the current criteria being too lenient, something that I wholeheartedly agree with

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Creating a typical textbook illustration of statistical power using either ggplot or base graphics

A common way of illustrating the idea behind statistical power in null hypothesis significance testing, is by plotting the sampling distributions of the null hypothesis and the alternative hypothesis. Typically, these illustrations highlight the regions that correspond to making a type II error, type I error and correctly rejecting the null hypothesis (i.e. the test’s power). In this post I will show how to create such “power plots” using both ggplot and R’s base graphics.

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