(NOTE: This ironic blog post was originally published on http://www.nicebread.de/introducing-p-hacker/)

 My Dear Fellow Scientists!

“If you torture the data long enough, it will confess.”

This aphorism, attributed to Ronald Coase, sometimes has been used in a dis-respective manner, as if it was wrong to do creative data analysis.

In fact, the art of creative data analysis has experienced despicable attacks over the last years. A small but annoyingly persistent group of second-stringers tries to denigrate our scientific achievements. They drag science through the mire.

These people propagate stupid method repetitions (also know as “direct replications”); and what was once one of the supreme disciplines of scientific investigation – a creative data analysis of a data set – has been crippled to conducting an empty-headed step-by-step pre-registered analysis plan. (Come on: If I lay out the full analysis plan in a pre-registration, even an undergrad student can do the final analysis, right? Is that really the high-level scientific work we were trained for so hard?).

They broadcast in an annoying frequency that p-hacking leads to more significant results, and that researchers who use p-hacking have higher chances of getting things published.

What are the consequence of these findings? The answer is clear. Everybody should be equipped with these powerful tools of research enhancement!

The Art of Creative Data Analysis

Some researchers describe a performance-oriented data analysis as “data-dependent analysis”. We go one step further, and call this technique data-optimal analysis (DOA), as our goal is to produce the optimal, most significant outcome from a data set.

I developed an online app that allows to practice creative data analysis and how to polish your p-values. It’s primarily aimed at young researchers who do not have our level of expertise yet, but I guess even old hands might learn one or two new tricks! It’s called “The p-hacker” (please note that ‘hacker’ is meant in a very positive way here. You should think of the cool hackers who fight for world peace). You can use the app in teaching, or to practice p-hacking yourself.

Please test the app, and give me feedback! You can also send it to colleagues: http://shinyapps.org/apps/p-hacker

The full R code for this Shiny app is on Github.

Train Your p-Hacking Skills: Introducing the p-Hacker App

Here’s a quick walk-through of the app. Please see also the quick manual at the top of the app for more details.

First, you have to run an initial study in the “New study” tab:

When you ran your first study, inspect the results in the middle pane. Let’s take a look at our results, which are quite promising:

After exclusion of this obvious outlier, your first study is already a success! Click on “Save” next to your significant result to save the study to your study stack on the right panel:

Sometimes outlier exclusion is not enough to improve your result.

Now comes the magic. Click on the “Now: p-hack!” tab – this gives you all the great tools to improve your current study. Here you can fully utilize your data analytic skills and creativity.

In the following example, we could not get a significant result by outlier exclusion alone. But after adding 10 participants (in two batches of 5), controlling for age and gender, and focusing on the variable that worked best – voilà!

Do you see how easy it is to craft a significant study?

Now it is important to show even more productivity: Go for the next conceptual replication (i.e., go back to Step 1 and collect a new sample, with a new manipulation and a new DV). Whenever your study reached significance, click on the Save button next to each DV and the study is saved to your stack, awaiting some additional conceptual replications that show the robustness of the effect.

Many journals require multiple studies. Four to six studies should make a compelling case for your subtile, counterintuitive, and shocking effects:

Honor to whom honor is due: Find the best outlet for your achievements!

My friends, let’s stand together and Make Science Great Again! I really hope that the p-hacker app can play its part in bringing science back to its old days of glory.

(A quick side note: Some so-called „data-detectives“ developed several methods for detecting p-hacking. This is a cowardly attack on creative data analyses. If you want to take a look at these detection tools,  check out the p-checker app. You can also transfer your p-hacked results to the p-checker app with a single click on the button ‘Send to p-checker’ below your study stack on the right side).

Best regards,

Ned Bicare, PhD

PS: A similar app can be found on FiveThirtyEight: Hack Your Way To Scientific Glory