The double-blinded placebo-controlled randomized trial have long been held as the gold standard in pharmacological research. Unfortunately, this design is impossible to mimic in clinical psychology. Even if we — (…) Read more

## New d3.js visualization: Understanding Significance Testing and Statistical Power

Here is a new visualization created in the same manner as my Cohen’s d vizualisation. This new visualization is an interactive display of classical null hypothesis significance testing and statistical (…) Read more

## 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. Read more

## New d3.js visualization: Interpreting Cohen’s d effect size

I have created a new visualization in D3. The purpose is to aid in the interpretation of Cohen’s d. The visualization presents Cohen’s d in the following ways: Visually, Cohen’s (…) Read more

## Calculating the Overlap of Two Normal Distributions Using Monte Carlo Integration

I read this post over at the blog Cartesian Faith about Probability and Monte Carlo methods. The post describe how to numerically intregate using Monte Carlo methods. I thought the (…) Read more

## Visualizing a One-Way ANOVA using D3.js

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 Read more

## 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. Read more

## 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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## Working with shapefiles, projections and world maps in ggplot

In this post I show some different examples of how to work with map projections and how to plot the maps using ggplot. Many maps that are using the default projection are shown in the longlat-format, which is far from optimal. Here I show how to use either the Robinson or Winkel Tripel projection. Read more

## Analytical and simulation-based power analyses for mixed-design ANOVAs

In this post I show some R-examples on how to perform power analyses for mixed-design ANOVAs. The first example is analytical—and adapted from formulas used in G*Power (Faul et al., 2007), and the second example is a Monte Carlo simulation. Read more