The following table provides critical values for Q (α, n), where α is the probability of incorrectly rejecting the suspected outlier and n is the number of samples in the data set.

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 …

What is Dixon's Q test? Definition, Q Table and step by step examples. How to run the Q test in simple steps. Stats made easy!

May 24, 2014 · The following tables provide the critical values for q(k, df, α) when α = .10, .05, .025, 01, .005 and .= 001. See Unplanned Comparisons for ANOVA for more details.

Q Test for Outliers

The Q Test Calculator is an essential statistical tool for identifying outliers in small datasets. By comparing the gap between a suspected value and its neighbors to the total data range, you can …

Use this table to detect statistical outliers using the Dixon's Q test.

Table 1, on page 2, lists critical values of Q at the 5% significance level. Notice that the smaller the number of data points, the larger Q must be for you to reject a data point.

Critical Values of Expanded Dixon Outlier Test (cont.) Taken from Verma and Quiroz-Ruiz, Table 2.

multiple outliers, but these are less frequently used than the single-outlier version. Finally, the article provides tables summari ing the limit values of the two-tailed Dixon's Q test for different sample sizes …