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:
“Combining paid employment with marriage and motherhood creates safeguards for emotional well-being. Nothing is certain in life, but generally the chances of happiness are greater if one has multiple areas of interest and involvement. To juggle is to diminish the risk of depression, anxiety, and unhappiness.”
—Faye J. Crosby (20th century)
“I don’t like comparisons with football. Baseball is an entirely different game. You can watch a tight, well-played football game, but it isn’t exciting if half the stadium is empty. The violence on the field must bounce off a lot of people. But you can go to a ball park on a quiet Tuesday afternoon with only a few thousand people in the place and thoroughly enjoy a one-sided game. Baseball has an aesthetic, intellectual appeal found in no other team sport.”
—Bowie Kuhn (b. 1926)