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All scientific interpretations of statistical outputs depend on background(auxiliary) assumptions that are rarely delineated or explicitly interrogated.These include not only the usual modeling assumptions, but also deeperassumptions about the data-generating mechanism that are implicit inconventional statistical interpretations yet are unrealistic in most health,medical and social research. We provide arguments and methods forreinterpreting statistics such as P-values and interval estimates inunconditional terms, which describe compatibility of observations with anentire set of underlying assumptions, rather than with a narrow targethypothesis conditional on the assumptions. Emphasizing unconditionalinterpretations helps avoid overconfident and misleading inferences in light ofuncertainties about the assumptions used to arrive at the statistical results.These include not only mathematical assumptions, but also those about absenceof systematic errors, protocol violations, and data corruption. Unconditionaldescriptions introduce assumption uncertainty directly into the primarystatistical interpretations of results, rather than leaving it for thediscussion of limitations after presentation of conditional interpretations.The unconditional approach does not entail different methods or calculations,only different interpretation of the usual results. We view use ofunconditional description as a vital component of effective statisticaltraining and presentation. By interpreting statistical outputs in unconditionalterms, researchers can avoid making overconfident statements based onstatistical outputs. Instead, reports should emphasize the compatibility ofresults with a range of plausible explanations, including assumptionviolations.

To Aid Scientific Inference, Emphasize Unconditional Compatibility Descriptions of Statistics