Peer to Peer

20 Mar

A peer is an equal, except as a reviewer, when they are more like capricious dictators. (The other possibility is that the person is a member of a peerage.)

We review our peers’ work because we know that we are all fallible. And because we know that the single best way we can overcome our own limitations is by relying on well-motivated, informed, others. We review to catch what our peers may have missed, to flag important methodological issues, to provide suggestions for clarifying and improving the presentation of results, among other such things. But given a disappointingly long history of capricious reviews, authors need assurance. So consider including in the next review a version of the following note:

Reviewers are authors too. And just as fallible, adjusting for time devoted to the task. So this review doesn’t come with the implied contract to follow all ill-advised things or suffer. If you disagree with something, I would appreciate a small note. But rejecting a bad proposal is as important as accepting a good one.

Fear no capriciousness. And I wish you well.

Bad Science: A Partial Diagnosis And Some Remedies

3 Sep

Lack of reproducibility is a symptom of science in crisis. An eye-catching symptom to be sure, but hardly the only one vying for attention. Recent analyses suggest that nearly two-thirds of the (relevant set of) articles published in prominent political science journals condition on post-treatment variables (see here.) Another set of analysis suggests that half of the relevant set of articles published in prominent neuroscience journals treat difference in significant and non-significant result as the basis for the claim that difference between the two is significant (see here). What is behind this? My guess: poor understanding of statistics, poor editorial processes, and poor strategic incentives.

  1. Poor understanding of statistics: It is likely the primary reason. For it would be good harsh to impute bad faith on part of those who use post-treatment variables as control or treating difference between significant and non-significant result as significant. There is likely a fair bit of ignorance — be it on the part of authors or reviewers. If it is ignorance, then the challenge doesn’t seem as daunting. Let us devise good course materials, online lectures, and teach. And for more advanced scholars, some outreach. (And it may involve teaching scientists how to write-up their results.)

  2. Poor editorial processes: Whatever the failings of authors, they aren’t being caught during the review process. (It would be good to know how often reviewers are actually the source of bad recommendations.) More helpfully, it may be a good idea to create small questionnaires before submission that alert authors about common statistical issues.

  3. Poor strategic incentives: If authors think that journals are implicitly biased towards significant findings, we need to communicate effectively that it isn’t so.

Stemming the Propagation of Error

2 Sep

About half of the (relevant set of) articles published in neuroscience mistake difference between a significant result and an insignificant result as evidence for the two being significantly different (see here). It would be good to see if the articles that make this mistake, for instance, received fewer citations post publication of the article revealing the problem. If not, we probably have more work to do. We probably need to improve ways by which scholars are alerted about the problems in articles they are reading (and interested in citing). And that may include building different interfaces for the various ‘portals’ (Google scholar, JSTOR etc., and journal publishers) that scholars heavily use. For instance, creating UIs that thread reproduction attempts, retractions, articles finding serious errors within the original article, etc.