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- W4301114443 abstract "A popular approach to significance testing proposes to decide whether the given hypothesized statistical model is likely to be true (or false). Statistical decision theory provides a basis for this approach by requiring every significance test to make a decision about the truth of the hypothesis/model under consideration. Unfortunately, many interesting and useful models are obviously false (that is, not exactly true) even before considering any data. Fortunately, in practice a significance test need only gauge the consistency (or inconsistency) of the observed data with the assumed hypothesis/model -- without enquiring as to whether the assumption is likely to be true (or false), or whether some alternative is likely to be true (or false). In this practical formulation, a significance test rejects a hypothesis/model only if the observed data is highly improbable when calculating the probability while assuming the hypothesis being tested; the significance test only gauges whether the observed data likely invalidates the assumed hypothesis, and cannot decide that the assumption -- however unmistakably false -- is likely to be false a priori, without any data." @default.
- W4301114443 created "2022-10-04" @default.
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- W4301114443 date "2013-01-07" @default.
- W4301114443 modified "2023-10-16" @default.
- W4301114443 title "Significance testing without truth" @default.
- W4301114443 doi "https://doi.org/10.48550/arxiv.1301.1208" @default.
- W4301114443 hasPublicationYear "2013" @default.
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