[jldugger]

Interesting approach -- get the scientific community to agree on the mathematical principles first, before anyone specifically is outed as cheating.

But this article feels like reading a teaser chapter of a bigger story.

> The amount of (toil) required to actually create data like this from scratch if (very) nightmarish. It’s a task drastically out of reach of the (foolish people) who’d try such a bush league stunt in the first place.

This assumes that all experiments lead to publications. We know there's a strong publication bias, and that the bias favors positive results, and dramatically favors unintuitive positive results. Which means you need to find correlations where none were expected. How many experiments do you need to get a significant correlation when there is none? Hint: more than one.

It's also worth noting that it wouldn't be difficult to produce a genetic algorithm using various statistical checks, including this one, as a fitness function.

[moyix]

Just to address your comment about publication bias – we do have techniques for detecting that across a set of studies on the same subject, namely funnel plots:

https://en.wikipedia.org/wiki/Funnel_plot

[jldugger]

Wasn't implying we don't. Just that the calculus of data fraud is weighted more towards fraud than the article suggests.