Outliers lend themselves to graphics perhaps more than any other aspect of statistics. See a comparison of these two methods here. The most popular alternative is called ROUT. There are additional outlier identification tests available in Prism. See our example that uses Grubbs' Test on a lognormal distribution. For example, in the biological sciences, data often follows a lognormal distribution, which looks at first to have obvious outliers if the pattern is not recognized appropriately. However, it's rare to observe "normal" data in the world. The second main limitation is that Grubbs' assumes the data was sampled from a normal (or gaussian) distribution. If there are multiple outliers close together, these "neighbors" can result in Grubbs' not labeling either an outlier. If you wish to see if there is more than one outlier after the first test, it is not appropriate to remove the first outlier and run Grubbs' again to look for more. It gives a general answer to the question "Is there at least one outlier in this data?". There are two main assumptions of Grubbs' Test that limit its practical usage.įirst, Grubbs' only looks for one outlier in the dataset. Notice that although the Grubbs' Test only determines if the most extreme value is an outlier, the entire dataset is used to calculate the mean and standard deviation for the test. If that P value is greater than alpha, the test concludes there is no evidence of an outlier in your dataset. The results page will then mark this data point as an outlier. 05), it is considered a significant outlier. The P value is interpreted like normality testing: If the P value corresponding to this Z is less than the alpha value chosen (such as. Once the value of Z is calculated for each data point, Grubbs' considers the largest value of Z in the dataset and calculates its P value. Interpreting results from Grubbs' Test is straightforward. Do not use a long list separated by commas!Ĭlick calculate to view the results, including basic descriptive statistics and, if there is one, which datapoint was identified as an outlier. Be sure to enter one data point on each line. Then copy and paste your data into the right side. Read more about Grubbs' test and its interpretation.įirst, choose the significance level (alpha) where an outlier will be detected. The test statistic corresponds to a p-value that represents the likelihood of seeing that outlier assuming the underlying data is Gaussian. It is based on a normal distribution and a test statistic (Z) that is calculated from the most extreme data point. Grubbs' Test, or the extreme studentized deviant (ESD) method, is a simple technique to quantify outliers in your study. That's quite a range, and it could be anywhere in between, too! Use our outlier checklist to help decide what to do in your case. They could be as simple as data entry errors or the outliers could themselves be an important research finding. It's best to think about outliers as points of interest, and what to do with them isn't straightforward. There are many reasons for outliers, and they can show up in any kind of study. It could be very large or very small, but it is abnormally different from most of the other values in your dataset. An outlier is a data point on the extreme end of your dataset.
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