## 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

## 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

## 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

## How to tell when error bars correspond to a significant p-value

Can you tell when error bars based on 95 % CIs or standard errors correspond to a significant p-value? Don’t fret if you think it’s hard, a study from 2005 showed that researchers in psychogoly, behavior neuroscience and medicine had a hard time judging when error bars from two independent groups signified a significant difference. Read more

## The Higgs boson: 5-sigma and the concept of p-values

Why are physicists talking about 5-sigma, and what’s it got to do with statistics? In this short post I’ll explain what 5-sigma is and why it’s not a measure of how certain scientist are that they’ve found the Higgs boson Read more

## Effect of sample size on the accuracy of Cohen’s d estimates (95 % CI)

When talking about confidence intervals, Jacob Cohen famously said: “I suspect that the main reason they are not reported is that they are so embarrassingly large!” (Cohen, 1994). In this post I’ll take a look at the relationship between the 95 % CI for Cohen’s d and it’s corresponding sample size. Read more

## PubMed publications in 2011 by 202 world countries: who’s the winner?

Which country had the most PubMed citations in 2011? To find out I used R statistical software to analyze the affiliation of 986 427 articles. Read more

## More PubMed data mining: looking at top 20 CBT journals

In this short article I present some data of the top 20 Cognitive Behavior Therapy (CBT) journals with the most PubMed publications, and compare that to data from 2010 and 2011. Read more

## Short R script to plot effect sizes (Cohen’s d) and shade overlapping area

In this short post I take a look at how to use R and ggplot2 to visualize effect sizes (Cohen’s d) and how to shade the overlapping area of two distributions. Read more