now online!

I’m very excited to announce that the statcheck web app created by Sean Rife and sponsored by Rackspace is online and ready for use! You can find the app at

Below you can find a short interview (Dutch with English subs) in which I explain what the app does.


statcheck 1.2.2 now on CRAN & statcheck manual on RPubs

The new statcheck 1.2.2* is now on CRAN!

Main updates:

  • Improved the regular expressions to avoid that statcheck wrongly recognizes weird statistics with subscripts as chi-squares
  • You can now choose whether to count “p = .000” as incorrect (this was default in the previous version)
  • The statcheck plot function now renders a plot in APA style (thanks to John Sakaluk for writing this code!)
  • Give pop-up window to choose a file when there is no file specified in “checkPDF()” or “checkHTML()”

For the full list of adaptations, see the History page on GitHub.

Besides the new updated package, I also created a detailed manual with instructions for installation and use of statcheck, including many examples and explanation of the output. You can find the manual on RPubs here.

* For the people who actually know what this numbering stands for: you may have noticed that the previous version on CRAN was version 1.0.2, so this seems like a weird step. It is. It’s because at first I had no idea what these numbers stood for (MAJOR.MINOR.PATCH), so I was just adding numbers at random. Actually the previous version should have been 1.1.x, which means that I’m now at 1.2.x. The last two PATCHES were because I messed up the R CMD check and had to fix some last minute things 🙂

statcheck: 5.000 downloads, BayesMed: 10.000 downloads

Thanks to Felix Schönbrodt’s code to track CRAN R package downloads, I was able to see how often my packages statcheck and BayesMed were downloaded.

It turns out: quite a lot!

BayesMed: 9665 downloads since January 2014
statcheck: 4930 downloads since November 2014

Furthermore, it is quite clear when most academics are on holiday:


BayesMed is a package to perform a default Bayesian hypothesis test for mediation, correlation, and partial correlation. For more information, click here.

statcheck extracts statistics from articles and recalculates the p-value. For more information, click here.

How can editors help prevent statistical errors? My new essay.

March 2016

There are too many statistical inconsistencies in published papers, and unfortunately they show a systematic bias towards reporting statistical significance.

Statistical reporting errors are not the only problem we are currently facing in science but at least it seems like one that is relatively easy to solve. I believe journal editors can play an important role in achieving change in the system, in order to slowly but steadily decrease statistical errors and improve scientific practice.

Nuijten, M.B. (2016). Preventing statistical errors in scientific journals. European Science Editing, 42, 1, 8-10.

You can find the post-print here.

Making error detection easier – and more automated: My guest post about statcheck on Retraction Watch

November 2015

I wrote a guest post for Retraction Watch about the development of Sacha’s and my R package “statcheck”, which automatically extracts statistical results and recomputes p-values.

Read it here:

“The prevalence of statistical reporting errors in psychology (1985-2013)” published at Behavior Research Methods

October 2015

In this paper we use the automated procedure “statcheck” to extract over 250.000 p-values from 30.000 psychology articles and check whether they are consistent.

We find that half of the articles contain at least one inconsistency, and 1 in 8 articles contains a gross inconsistency that affects the statistical conclusion. The prevalence of inconsistencies seems to be stable over time.

The article is Open Access and available here:

“The prevalence of statistical reporting errors in psychology (1985-2013)” accepted for publication at Behavior Research Methods

September 2015

In this manuscript we use the R package statcheck (by Sacha Epskamp & me) to examine the prevalence of statistical reporting errors in 8 major psychology journals from 1985 to 2013. We find that half of all articles contains at least one inconsistency and 1 in 8 articles contains a grossly inconsistent p-value that could have changed the statistical conclusion. We find no evidence that the prevalence of inconsistencies is increasing over the years.

You can find the post-print here: PDF