Often enough, scientists are left with the unenviable task of conducting an orchestra with out-of-tune instruments. They are charged with telling a coherent story about noisy results. Scientists defer to the demand partly because there is a widespread belief that a journal article is the appropriate grouping variable at which results should ‘make sense.’
To tell coherent stories with noisy data, scientists resort to a variety of underhanded methods. The first is simply squashing the inconvenient results—never reporting them or leaving them to the appendix or couching the results in the language of the trade, e.g., “the result is only marginally significant” or “the result is marginally significant” or “tight confidence bounds” (without ever talking about the expected effect size). Secondly, if good statistics show uncongenial results, drown the data in bad statistics, e.g., report the difference between a significant and an insignificant effect as significant. The third trick is overfitting. A sin in machine learning is a virtue in scientific storytelling. Come up with fanciful theories that could explain the result and make that the explanation. The fourth is to practice the “have your cake and eat it too” method of writing. Proclaim big results at the top and offer a thick word soup in the main text. The fifth is to practice abstinence—abstain from interpreting ‘inconsistent’ results as coming from a lack of power, bad theorizing, or heterogeneous effects.
The worst outcome of all of this malaise is that many (expectedly) become better at what they practice—bad science and nimble storytelling.