In statistics, an outlier is generally defined as a data point that is significantly different from other values in a dataset. A common rule of thumb for identifying outliers is:

• Any data point that is more than 3 standard deviations away from the mean (either above or below) is considered a potential outlier.

This is based on properties of the normal distribution, where about 99.7% of the data falls within 3 standard deviations of the mean, meaning that anything beyond that range is unusual.

Summary:

• Above the mean: Data point > 3 standard deviations above the mean.
• Below the mean: Data point < 3 standard deviations below the mean.

Would you like more details on how this relates to different types of data distributions?

Here are five related questions you may find interesting:

1. How do you calculate standard deviation from a dataset?
2. What is the difference between a mild outlier and an extreme outlier?
3. How does skewness affect the identification of outliers?
4. What are other methods besides standard deviation to detect outliers?
5. How can removing outliers affect the results of statistical analysis?

Tip: Always visualize your data using a boxplot or scatter plot to easily spot potential outliers.