The number of letters received in the mail over the past week is recorded.

2    3    5    5    5    15    

Which value appears to be an outlier?

The data set you provided is:

2, 3, 5, 5, 5, 15.

To identify the outlier, we can observe that most of the values are clustered around 2 to 5, while 15 is significantly higher than the others. This large deviation makes **15** appear to be the outlier.

Would you like to see a more formal method to detect this outlier, such as calculating the interquartile range (IQR) or using standard deviation?

Here are 5 questions related to outliers:

1. What are some formal methods for detecting outliers in a data set?
2. How does the interquartile range (IQR) help in identifying outliers?
3. Why is it important to detect outliers in a statistical analysis?
4. Can outliers affect the mean and median of a data set? How?
5. Are there any scenarios where an outlier should not be removed from a data set?

**Tip:** Always examine the context of the data before deciding to remove or address outliers. Sometimes outliers are legitimate and provide valuable insights.

The number of letters received in the mail over the past week is recorded: 2, 3, 5, 5, 5, 15. Which value appears to be an outlier?

Explore how to identify outliers in a data set with a practical example of mail letters received. Learn statistical concepts like outliers, interquartile range (IQR), and standard deviation. Suitable for students in Grades 6-8.

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