Outliers
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last modified on 2/6/07

email comments to harvey@depauw.edu or william.otto@maine.edu

Summary

After completing this module you should understand what it means for a result to be a possible outlier and understand how to consider whether it is possible to reject the apparent outlier.

If you suspect that a result is an outlier you should first look carefully at the sample and your work. Is there convincing evidence that the sample, or your analysis of the sample, is fundamentally different from other samples? A penny coated with a green discoloration due to oxidative corrosion should be discarded if it being compare to pennies that are free from such corrosion. A sediment sample containing relatively large solid particles, such as pebbles, should not be included with samples that consist of only fine grain particles. If you overshoot the endpoint during a titration, you should discard the result. Do not retain samples or results in these, or similar, circumstances just because the result happens to make your overall results look better.

If there is no convincing visible evidence for discarding an outlier, consider using the Q-test. Be cautious in its use, however, since you may be eliminating a valid result.

Additional information on the topics covered in this module is available using the link on the left for further study, or return to the Data Analysis home page to explore other modules.