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R, STATISTICS, PSYCHOLOGY, OPEN SCIENCE, DATA VISUALIZATION

## Articles with the statistics tag

New visualization of the distribution of p-values using d3.js

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?

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.

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

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

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

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.