![]() Most values are centered around the middle, as expected. Your data are normally distributed with a couple of outliers on either end. Example: True outlierYou measure 100-meter running times for a representative sample of 560 college students. ![]() True outliers should always be retained in your dataset because these just represent natural variations in your sample. What you should do with an outlier depends on its most likely cause. Other outliers may result from incorrect data entry, equipment malfunctions, or other measurement errors.Īn outlier isn’t always a form of dirty or incorrect data, so you have to be careful with them in data cleansing. Some outliers represent true values from natural variation in the population. Outliers are values at the extreme ends of a dataset.
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