Software to check texts for spelling errors is commonplace, but catching errors of a more technical nature, such as incorrect P‐value calculations, is still a manual endeavor. Nonetheless, text‐mining technology to catch a growing number of error types within scientific manuscripts has been developed by studies interested in broad, literature‐wide surveys. The same algorithms that are now used to retrospectively identify potential errors in published papers can also be used pre‐emptively to identify errors before publication. So far, these algorithms have focused on finding errors of commission, such as incorrect calculations, but could also find errors of omission, such as leaving out details needed to reproduce the results. This could offer many advantages for those aspects of peer review that are amenable to double‐checking by an algorithm: consistency, uniformity, speed, cost efficiency, and reducing the growing burden on peer reviewers.
Scientists are well aware that publications are not necessarily error‐free, even if they have undergone peer review. To some extent, the publication record is self‐correcting by virtue of new published findings, but we still rely upon the scientific literature as our most authoritative source of established knowledge. Thus, minimizing errors in publications is important, but how important it is depends on how frequent the errors are, their magnitude, and the probability they will lead to erroneous conclusions or wasted effort in reproducing results. Recent studies have begun to address the first two issues by quantifying the frequency and magnitude of mathematical and statistical discrepancies in the literature algorithmically. This creates an interesting situation: if discrepancies/errors can be found algorithmically after publication, they can also, in theory, be found and corrected before publication. The purpose of this commentary is to discuss errors from the standpoint of studying them systematically, summarize what some of …
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