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In statistics, Dixon's Q test, or simply the Q test, is used for identification and rejection of outliers.This assumes normal distribution and per Robert Dean and Wilfrid Dixon, and others, this test should be used sparingly and never more than once in a data set.
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The modified Thompson Tau test is a method used to determine if an outlier exists in a data set. [23] The strength of this method lies in the fact that it takes into account a data set's standard deviation, average and provides a statistically determined rejection zone; thus providing an objective method to determine if a data point is an outlier.
The q-value can be interpreted as the false discovery rate (FDR): the proportion of false positives among all positive results. Given a set of test statistics and their associated q-values, rejecting the null hypothesis for all tests whose q-value is less than or equal to some threshold ensures that the expected value of the false discovery rate is .
Its statistical distribution is the studentized range distribution, which is used for multiple comparison procedures, such as the single step procedure Tukey's range test, the Newman–Keuls method, and the Duncan's step down procedure, and establishing confidence intervals that are still valid after data snooping has occurred.
Dixon's method is based on finding a congruence of squares modulo the integer N which is intended to factor. Fermat's factorization method finds such a congruence by selecting random or pseudo-random x values and hoping that the integer x 2 mod N is a perfect square (in the integers):
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The Q-statistic or q-statistic is a test statistic: The Box-Pierce test outputs a Q-statistic (uppercase) which follows the chi-squared distribution The Ljung-Box test is a modified version of the Box-Pierce test which provides better small sample properties