the replication crisis has redrawn the topography of science, especially in social psychology
Their substantive theory is so open-ended that it can explain just about any result, any interaction in any direction.
And that’s why the authors’ claim that fixing the errors “does not change the conclusion of the paper” is both ridiculous and all too true. It’s ridiculous because one of the key claims is entirely based on a statistically significant p-value that is no longer there. But the claim is true because the real “conclusion of the paper” doesn’t depend on any of its details—all that matters is that there’s something, somewhere, that has p less than .05, because that’s enough to make publishable, promotable claims about “the pervasiveness and persistence of the elderly stereotype” or whatever else they want to publish that day.
When the authors protest that none of the errors really matter, it makes you realize that, in these projects, the data hardly matter at all.
When it comes to pointing out errors in published work, social media have been necessary. There just has been no reasonable alternative. Yes, it’s sometimes possible to publish peer-reviewed letters in journals criticizing published work, but it can be a huge amount of effort. Journals and authors often apply massive resistance to bury criticisms.
when statistical design analysis shows that this research is impossible, or when replication failures show that published conclusions were mistaken, then damn right I expect you to move forward, not keep doing the same thing over and over, and insisting you were right all along.
We learn from our mistakes, but only if we recognize that they are mistakes. Debugging is a collaborative process. If you approve some code and I find a bug in it, I’m not an adversary, I’m a collaborator. If you try to paint me as an “adversary” in order to avoid having to correct the bug, that’s your problem.
Source: What has happened down here is the winds have changed – Statistical Modeling, Causal Inference, and Social Science