Page 1 To Aid Scientific Inference, Emphasize Unconditional Descriptions of Statistics Sander Greenland* Department of Epidemiology and Department of Statistics, University of California, Los Angeles, CA, U.S.A.
[email protected] Zad Rafi Department of Population Health, NYU Langone Medical Center, New York, NY, U.S.A. 27 February 2021 *Corresponding Author Suggested Running Head: Emphasize Unconditional Descriptions Conflict of Interest Disclosure: The authors declare no conflicts of interest. Page 2 Abstract: All scientific interpretations of statistical outputs depend on background (auxiliary) assumptions that are rarely delineated or checked in full detail. These include not only the usual modeling assumptions, but also deeper assumptions about the data-generating mechanism that are implicit in conventional statistical interpretations yet are unrealistic in most health, medical and social research. We provide arguments and methods for reinterpreting statistics such as P- values and interval estimates in unconditional terms, which describe compatibility of observations with an entire set of analysis assumptions, rather than just a narrow target hypothesis. Such reinterpretations help avoid overconfident inferences whenever there is uncertainty about the assumptions used to derive and compute the statistical results. These include assumptions about absence of systematic errors, protocol violations, and data corruption. Unconditional descriptions introduce assumption uncertainty directly into statistical interpretations of results, rather than leaving that uncertainty to caveats about conditional interpretations or limited sensitivity analyses. By interpreting statistical outputs in unconditional terms, researchers can avoid making overconfident statements for statistical outputs when there are uncertainties about assumptions, and instead emphasize the compatibility of the results with a range of plausible explanations. We thus view unconditional description as a vital component of good statistical training and presentation.