ICES REPORT 12 - 34 August 2012 Significance testing without

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. Essentially, all models are wrong, but some are useful. — G. E. P. Box