The Dixon Q-test | When to Discard Outliers in Your Data

The video tutorial explains how to assess the precision of a data set using parts per thousand and identifies outliers through the Dixon Q test. It details the process of calculating variance and range, and how to compare Q experimental and Q critical values to determine if an outlier can be discarded. The tutorial also demonstrates repeating the Dixon Q test for additional data points.

Describe the steps to calculate the variance in a data set.

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What does it mean if the Q experimental is greater than the Q critical?

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What should be done if the Q experimental is less than the Q critical?

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