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High Quality Content by WIKIPEDIA articles! In statistics, the multiple comparisons, or multiple testing, problem occurs when one considers a set, or family, of statistical inferences simultaneously.[1] 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 family 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.