By Tania Lombrozo
Originally published June 2, 2014
Here is an excerpt:
Researchers who engage in p-diligence are those who engage in practices — such as additional analyses or even experiments — designed to evaluate the robustness of their results, whether or not these practices make it into print. They might, for example, analyze their data with different exclusion criteria — not to choose the criterion that makes some effect most dramatic but to make sure that any claims in the paper don't depend on this potentially arbitrary decision. They might analyze the data using two statistical methods — not to choose the single one that yields a significant result but to make sure that they both do. They might build in checks for various types of human errors and analyze uninteresting aspects of the data to make sure there's nothing weird going on, like a bug in their code.
If these additional data or analyses reveal anything problematic, p-diligent researchers will temper their claims appropriately, or pursue further investigation as needed. And they'll engage in these practices with an eye toward avoiding potential pitfalls, such as confirmation bias and the seductions of p-hacking, that could lead to systematic errors. In other words, they'll "do their p-diligence" to make sure that they — and others — should invest in their claims.
P-hacking and p-diligence have something in common: Both involve practices that aren't fully reported in publication. As a consequence, they widen the gap. But let's face it: While the gap can (and sometimes should) be narrowed, it cannot be closed.
The entire article is here.
Thanks to Ed Zuckerman for this lead.