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The image shows a list of numbers representing the number of games won by 50 members of a chess club. The question asks for the most appropriate measure to describe the variability in the data distribution, with options including:

- Mean
- Median
- Standard Deviation
- Interquartile Range (IQR)

The selected answer is "Interquartile Range (IQR)," which is indeed a good choice. The IQR is often used to describe variability when the data is skewed or contains outliers, as it measures the spread of the middle 50% of the data (between the first and third quartile). This makes it less sensitive to extreme values compared to the standard deviation.

Do you want further details on calculating the IQR for this dataset or have any other questions?

Here are 5 relative questions for further exploration:
1. How is the Interquartile Range (IQR) calculated from raw data?
2. Why is IQR a better measure than standard deviation in datasets with outliers?
3. How can you use a box plot to visually display the IQR and detect outliers?
4. What would happen if the dataset had no outliers—would the IQR still be preferred over standard deviation?
5. How does the skewness of a dataset affect the choice between IQR and standard deviation?

**Tip**: IQR can be useful when you want to focus on the "central bulk" of your data and ignore outliers that could distort other measures like the mean or standard deviation.

The chess club at a school has 50 members. The number of games won in tournament play this season by each member is listed. What measure is most appropriate for describing variability in this data distribution?

Learn how to describe the variability in a dataset using the interquartile range (IQR). This problem covers key statistical concepts such as the IQR and why it's more appropriate for skewed data than standard deviation. Suitable for students in grades 6-8.

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