## Which option should you use if your data is skewed or has outliers?

### If your data is skewed or has outliers, it is generally recommended to use the median as a measure of central tendency instead of the mean

If your data is skewed or has outliers, it is generally recommended to use the median as a measure of central tendency instead of the mean. The median is less sensitive to extreme values and outliers compared to the mean.

The mean is influenced by extreme values, which can distort the overall average. For example, in a dataset with skewed data, the mean can be pulled towards the direction of the skewness, giving an inaccurate representation of the typical value. Outliers, which are unusually large or small values, can dramatically affect the mean as well.

On the other hand, the median represents the middle value in a sorted dataset. It is not affected by extreme values or outliers, as it only considers their position in relation to other values. Thus, the median provides a more robust measure of central tendency when the data is skewed or contains outliers.

So, if your data exhibits skewness or contains outliers, it is advisable to use the median instead of the mean for a more accurate representation of the data’s central tendency.

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