01492nas a2200181   4500000000100000008004100001260003400042653001700076653002100093100001900114700001100133700001500144700001600159245008200175856023600257520079200493022002501285       2021                            d  bOxford University Press (OUP)10aEpidemiology10aGeneral Medicine1 aBraithwaite RS1 aBan K(1 aStevens ER1 aCaniglia EC00aRounding up the usual suspects: confirmation bias in epidemiological research  uhttps://scholar.google.com/scholar_url?url=https://academic.oup.com/ije/advance-article-pdf/doi/10.1093/ije/dyab091/37629897/dyab091.pdf&hl=en&sa=T&oi=ucasa&ct=ufr&ei=fgOZYK-9NMPtmQH3_qPYDg&scisig=AAGBfm2gsUZ_N55s3-gCCoJLquv01yDt-g3 aInvestigators performing epidemiological research frequently form hypotheses based on data availability. One might ask how it could be otherwise. After all, what is the point of forming hypotheses if they can’t be tested? But when questions are identified to suit available data rather than data being identified to suit important questions, commonalities in measured and unmeasured variables extend across multiple studies and lead to a confirmation bias. Expected relationships are confirmed, and unexpected relationships remain undiscovered, even when their unveiling would have important informational value. We argue that this confirmation bias results from a structural cause, in particular misalignment of epidemiological research priorities with the social utility of research.  a0300-5771, 1464-3685