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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 .
In statistics, Dixon's Q test, or simply the Q test, ... To apply a Q test for bad data, arrange the data in order of increasing values and calculate Q as defined:
The p-values of the rejected null hypothesis (i.e. declared discoveries) are colored in red. Note that there are rejected p-values which are above the rejection line (in blue) since all null hypothesis of p-values which are ranked before the p-value of the last intersection are rejected. The approximations MFDR = 0.02625 and AFDR = 0.00730, here.
The value q s is the sample's test statistic. (The notation | x | means the absolute value of x; the magnitude of x with the sign set to +, regardless of the original sign of x.) This q s test statistic can then be compared to a q value for the chosen significance level α from a table of the studentized range distribution.
Q factor (bicycles), the width between where a bicycle's pedals attach to the cranks; q-value (statistics), the minimum false discovery rate at which the test may be called significant; Q value (nuclear science), a difference of energies of parent and daughter nuclides; Q Score, in marketing, a way to measure the familiarity of an item
A statistical hypothesis test typically involves a calculation of a test statistic. Then a decision is made, either by comparing the test statistic to a critical value or equivalently by evaluating a p-value computed from the test statistic. Roughly 100 specialized statistical tests have been defined. [1] [2]
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; The Tukey-Kramer test outputs a q-statistic (lowercase), also called the studentized range statistic, which follows the studentized range ...
Cochran's test is a non-parametric statistical test to verify whether k treatments have identical effects in the analysis of two-way randomized block designs where the response variable is binary. [ 1 ] [ 2 ] [ 3 ] It is named after William Gemmell Cochran .