Metrics of meaningfulness as opposed to sleights of significance

摘要

For some 80 years, statistical analyses have been predicated on the testing of hypotheses and evaluated by the probabilities of outcomes. This reflects Karl Popper's principle of falsifiability that states: before something can be accepted, the opposite has to be shown to be untenable. By convention, two principal hypotheses are usually stated: a research hypothesis - i.e. "A influences B" - and a null hypothesis - i.e. "A has no influence on B". It is the latter that is tested. Only when the null hypothesis is shown to be untenable can the research hypothesis be accepted. Usually, a probability of 0.05 is used as a cut-off. Probabilities equal to or less than 0.05 are considered to be statistically significant, whereas those greater than 0.05 are not.