Over the summer I've been working on finishing my new R package 'powerlmm', which is now almost complete. It provides flexible power calculations for typical two- and three-level longitudinal linear mixed models, with unbalanced treatment groups and cluster sizes, as well as with missing data and random slopes at both the subject and cluster-level.

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This post explains how Cohen's d relates to the proportion of overlap between two normal distributions, and why I use a different measure then Cohen in my Cohen's d visualization.

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This visualization illustrates the appropriate tests to use when your research hypothesis is that two treatments are equally effective, or that a new treatment is no worse than the current gold standard.

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The visualization illustrates a Bayesian two-sample t test

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I've added more options to my NHST visualization

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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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