Label Here you can identify if there are potential outliers in your data sample. The non-existence of outliers is an assumption of many statistical tests. Outliers can be caused by an error in the data collection, therefore it is always useful to look for outliers in your data and then investigate if there is really a problem with it. Typical applications are:

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You measured the transaction time of your process and now, starting your statistical analysis, you want to know if there are potential outliers in your data.

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As a supervisor of an administrative process, you have developed a different method to perform a task. Now you wish to confirm statistically if the average time of the new method is better. Prior to that, did you check if there are outliers in your data?

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You measured the interarrival time at a supermarket cashier. Is there any measured value that does not belong to the same probability distribution of the process?

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Label Here we perform 2 tests for outliers based on non-parametric statistical approaches for univariate data. It means it works for Normal or non-Normal distributed data.

Label Just enter your data as described below.

Step 1) Paste your sample data in the text box.

(up to 2000 numbers separated by at least one space)

Ex: 113.47 86.62 91.99 86.56 111.51 84.44 78.73 111.06 91.53 107.23