In combination, these factors create incentives for researchers to selectively pursue and selectively attempt to publish statistically significant research findings. Employers and funders often count papers and weigh them by the journal’s impact factor to assess a researcher’s performance.

Many argue that current scientific practices create strong incentives to publish statistically significant (i.e., “positive”) results, and there is good evidence that journals, especially prestigious ones with higher impact factors, disproportionately publish statistically significant results. An even less ethical practice is denoted by the expression “cherry-picking,” usually referring to biased selection of patients, analysis of subsets of data, or reporting of results favorable to the interests of a researcher, organization, or sponsor.There is increasing concern that many published results are false positives (but see ). The scientific validity of data dredging is dubious, usually unacceptable. Such analyses are sometimes done when data have been collected on a large number of variables and unanticipated hypotheses are suggested by hypothesis-free analyses. Under the terms of the licence agreement, an individual user may print out a PDF of a single entry from a reference work in OR for personal use (for details see Privacy Policy and Legal Notice).ĭate: 16 August 2023 Data Dredging Source: A Dictionary of Epidemiology Author(s): Miquel PortaĪ jargon term meaning analyses done on a post hoc basis without benefit of prestated hypotheses. PRINTED FROM OXFORD REFERENCE ((c) Copyright Oxford University Press, 2023. Drug-Resistant Tuberculosis, Extensively.Drug-Resistant Tuberculosis, Multiple (MDR Tuberculosis).Direct Adjustment, Direct Standardization.Diagnostic and Statistical Manual of Mental Disorders (DSM).Developmental Origins of Health and Disease.Developmental and Life Course Epidemiology.

Data dredging bias