Dixon's Q-test Calculator - Detection of a single outlier
Dixon' Q test calculator Dixon's Q-test Calculator - Detection of a single outlier Dixon’s test (or the Q-test ) has been described in a previous post entitled “ Detection of a Single Outlier|Statistical Analysis|Quantitative Data ”. The test is popular because the calculations involved are simple. A solved example is given in the above post. Are there any limitations to Dixon’s Q-test? The data excluding the possible outlier must be normally distributed (use the Kolmogorov-Smirnov test to check if data is normally distributed ) The Q-test is valid for the detection of a single outlier (it cannot be used for a second time on the same set of data). Other forms of Dixon’s Q-test can be applied to the detection of multiple outliers. The Q-test should be applied with caution – the same applies to all statistical tests used for rejecting data - since there is a probability, equal to the significance level a (a =...