In statistics, the multiple comparisons, multiplicity or multiple testing problem occurs when one considers a set of statistical inferences simultaneously. or infer on selected parameters only, where the selection depends on the observed values. Errors in inference, including confidence intervals that fail to include their corresponding population parameters or hypothesis tests that incorrectly reject the null hypothesis are more likely to occur when one considers the set as a whole. Several statistical techniques have been developed to prevent this from happening, allowing significance levels for single and multiple comparisons to be directly compared. These techniques generally require a stronger level of evidence to be observed in order for an individual comparison to be deemed "significant", so as to compensate for the number of inferences being made.
Read more about Multiple Comparisons: History, The Problem, Example: Flipping Coins, What Can Be Done, Methods, Post-hoc Testing of ANOVAs, Large-scale Multiple Testing
Famous quotes containing the words multiple and/or comparisons:
“... the generation of the 20’s was truly secular in that it still knew its theology and its varieties of religious experience. We are post-secular, inventing new faiths, without any sense of organizing truths. The truths we accept are so multiple that honesty becomes little more than a strategy by which you manage your tendencies toward duplicity.”
—Ann Douglas (b. 1942)
“The surest route to breeding jealousy is to compare. Since jealousy comes from feeling “less than” another, comparisons only fan the fires.”
—Dorothy Corkville Briggs (20th century)