Understand confidence intervals by using my new interactive visualization.

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This new visualization is an interactive display of the correlation between two variables.

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The notion is fairly well spread that wait-lists could act as a nocebo condition in psychotherapy trials. In this post I write about some recent results from a network meta-analysis that investigated this.

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This new visualization is an interactive display of classical null hypothesis significance testing and statistical power.

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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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I have created a new visualization in D3. The purpose is to aid in the interpretation of Cohen’s d.

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Last week a group of Dutch scientists published a study providing further evidence of mindfulness’ ability to bolster creativity. Specifically they looked at if open awareness differed from focused attention in increasing divergent thinking

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A while ago I was playing around with the javascript package D3.js, and I began with this visualization—that I never really finished—of how a one-way ANOVA is calculated. I tried to make it look like a plot from ggplot2 except with interactive elements. Take a look at it after the jump

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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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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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