Featured App

p-hacker: Train your p-hacking skills!

by Felix Schönbrodt

Train your expert p-hacking skills with the interactive p-hacker app! Who gets p<.05 first?

Shiny widgets Experience statistics

Exploring the diagnosticity of the p-value

by Erik-Jan van Kesteren and Eric-Jan Wagenmakers

Explore the Vovk-Sellke Maximum p-Ratio, a measure that indicates the maximum diagnosticity of a given p-value. Choose your own p-value to find out how diagnostic it is for your research!

P-value distribution and power curves for an independent two-tailed t-test

by Daniel Lakens

Plot the theoretical p-value distribution and power curve for an independent t-test based on the effect size, sample size, and alpha.

Univariate k-Means Clustering with elbow method

by Jan Freyberg

Identify how many clusters your one-dimensional data can be grouped in and how much variance you can explain with these clusters by using the "elbow method".

BIC approximation for ANOVA designs

by Christoph Huber-Huber

Obtain a Bayesian interpretation of your ANOVA results with this app. You just need to enter your sum of squares and some information about your design.

N per discovery

by Etienne LeBel

App to explore the cost-effectiveness of different research approaches to unearth true scientific discoveries.

2D Outlier analysis

by Rajiv Shah

The app allows you to see the trade-offs on various types of outlier/anomaly detection algorithms. Outliers are marked with a star and cluster centers with an X.

When does a significant p-value indicate a true effect?

by Michael Zehetleitner and Felix Schönbrodt

Understanding the Positive Predictive Value (PPV) of a p-value.

p-checker: The one-for-all p-value analyzer

by Felix Schönbrodt

This Shiny app implements the p-curve (Simonsohn, Nelson, & Simmons, 2014; see http://www.p-curve.com) in its previous ("app2") and the current version ("app3"), the R-Index and the Test of Insufficient Variance, TIVA (Schimmack, 2014; see http://www.r-index.org/), and tests whether p values are reported correctly.

What does a Bayes factor look like? (2) Height difference between males and females.

by Felix Schönbrodt

Can you "see" a group mean difference, just by eyeballing the data? Is your gut feeling aligned to the formal index of evidence, the Bayes factor?

What does a Bayes factor look like? (1) The urn model.

by Felix Schönbrodt

How much is a BF of 3.7? Well, it is "moderate evidence" for an effect - whatever that means. Let's approach the topic a bit more experientially. What does such a BF look like, visually? We take the good old urn model as an example.

A First Lesson in Bayesian Inference

by Eric-Jan Wagenmakers

This app highlights several key Bayesian concepts.

A Compendium of Clean Graphs in R

by Eric-Jan Wagenmakers

When done right, graphs can be appealing, informative, and of considerable value to an academic article. This compendium facilitates the creation of good graphs by presenting a set of concrete examples, ranging from the trivial to the advanced. The graphs can all be reproduced and adjusted by copy-pasting code into the R console.

Bayes factor robustness

by Felix Schönbrodt

Check how your Bayes factor conclusion depends on the r-scale parameter.

Polynomial Surface Explorer

by Felix Schönbrodt

Adjust regression parameters to bend and shift a two-dimensional polynomial surface.


by Felix Schönbrodt

Be an optimizer: Find the best linear fit!

Robustness of Mean and Median

by Tobias Kächele

Which is more robust against outliers: Mean or median?

Normal distribution vs. binomial

by Tobias Kächele

Approximating a normal distribution with a binomial distribution

Production errors

by Tobias Kächele

Get acquainted with histograms.