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Arthur T Knackerbracket has found the following story:

They say that hindsight is 20-20, and perhaps nowhere is that more true than in academic research.

"We've all had the experience of standing up to present a novel set of findings, often building on years of work, and having someone in the audience blurt out 'But we knew this already!,'" says Prof. Stefano DellaVigna, a behavioral economist with joint appointments in the Department of Economics and Berkeley Haas. "But in most of these cases, someone would have said the same thing had we found the opposite result. We're all 20-20, after the fact."

DellaVigna has a cure for this type of academic Monday morning quarterbacking: a prediction platform to capture the conventional wisdom before studies are run.

Along with colleagues Devin Pope of the University of Chicago's Booth School of Business and Eva Vivalt of the Research School of Economics at Australian National University, he's launched a beta website that will allow researchers, PhD students, and even members of the general public to review proposed research projects and make predictions on the outcome.

[...]Their proposal, laid out in a new article in Science's Policy Forum, is part of a wave of efforts to improve the rigor and credibility of social science research. These reforms were sparked by the replication crisis -- the failure of reproduce the results of many published studies -- and include mass efforts to replicate studies as well as platforms for pre-registering research designs and hypotheses.

"We thought there was something important to be gained by having a record of what people believed before the results were known, and social scientists have never done that in a systematic way," says DellaVigna, who co-directs the Berkeley Initiative for Behavioral Economics and Finance. "This will not only help us better identify results that are truly surprising, but will also help improve experimental design and the accuracy of forecasts."

Journal Reference:

Stefano DellaVigna, Devin Pope, Eva Vivalt. Predict science to improve science. Science, 2019; 366 (6464): 428 DOI: 10.1126/science.aaz1704

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