Multiple Comparisons Inference Task

A Multiple Comparisons Inference Task is a statistical inference task that occurs when multiple sets of parameters are considered simultaneously.

• AKA: Multiplicity Testing Problem, Look-Elsewhere Effect.
• Context:
  • It can be solved by a Multiple Comparisons Inference System (that implements a multiple comparisons inference algorithm).
• See: Statistical Test, Null Hypothesis, Confidence Interval, Family-Wise Error Rate.

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References

2016

• (Wikipedia, 2016) ⇒ http://en.wikipedia.org/wiki/Multiple_comparisons_problem Retrieved 2016-08-21
  • In statistics, the multiple comparisons, multiplicity or multiple testing problem occurs when one considers a set of statistical inferences simultaneously or infers a subset of parameters selected based on the observed values. It is also known as the look-elsewhere effect.

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 higher significance threshold for individual comparisons, so as to compensate for the number of inferences being made.

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