My p-curve visualization has been updated with the option to log the x-axis.

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This visualization shows how the t-distribution and the Gaussian (normal) distribution approaches each other when sample size increases.

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I often get asked about how to fit different longitudinal models in lme/lmer. In this post I cover several different two-level, three-level and partially nested models.

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Here's a new visualization that shows the p-curve distribution when comparing the means of two independent samples for varying effects. Many know that the distribution is uniform when the null is true, but what about when it isn't?

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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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