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

This app allows to identify potential microRNA-34 targets by combining computational miRNA target prediction resources with experimental transcriptomic and proteomic datasets derived from cell lines, mouse models, and tumor tissues.

This Shiny app helps researchers with sample size planning for Bayesian independent-samples t-tests, using Bayes Factor Design Analysis (BFDA), a simulation-based framework for Bayesian power analyses.

This app generates 10 waves of data for two variables, based on a bivariate "stable trait, autoregressive trait, state" (STARTS) model and then runs a standard cross-lagged panel model, and a random-intercept cross-lagged panel model on the data. The purpose of the app is to examine how the estimated cross-lagged paths in the standard cross-lagged panel model and the random-intercept cross-lagged panel model are affected when stable-trait or state variance exists but is omitted from the model.

Ever wondered why p-values are uniformly distributed if the H0 is accurate, even though the data are normally distributed? This animated app visualizes the connection of the two via the CDF and lets you explore the relationship between effect size and skewness of the distribution of p-values.

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!

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

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".

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.

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

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.

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

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.

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?

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.

This app highlights several key Bayesian concepts.

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.

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

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

Which is more robust against outliers: Mean or median?