Question

Compare the differences between when the interquartile range should be used to compare measures of spread and when the range should be used.(1 point)

Responses

The interquartile range should be used when all of the data sets are symmetric and do not have outliers. The range should be used when at least one of the data sets is skewed or has outliers.
The interquartile range should be used when all of the data sets are symmetric and do not have outliers. The range should be used when at least one of the data sets is skewed or has outliers.

The interquartile range should be used when one or more of the data sets are skewed or have outliers. The range should be used when all of the data sets do not have any outliers.
The interquartile range should be used when one or more of the data sets are skewed or have outliers. The range should be used when all of the data sets do not have any outliers.

The interquartile range should be used when all of the data sets do not have outliers. The range should be used when at least one of the data sets has outliers.
The interquartile range should be used when all of the data sets do not have outliers. The range should be used when at least one of the data sets has outliers.

The interquartile range should only be used when all of the data sets are skewed or have outliers. The range should be used when at most one of the data sets has outliers.
The interquartile range should only be used when all of the data sets are skewed or have outliers. The range should be used when at most one of the data sets has outliers.

Answer 1

[AI-generated answer — Bot GPT 3.5]

In most cases, the interquartile range should be used when comparing measures of spread because it is less affected by outliers and skewness compared to the range. The range gives the full extent of the data values, including outliers, which can skew the results and may not provide an accurate representation of the spread of the data. The interquartile range focuses on the middle 50% of the data, making it more robust and reliable when outliers are present.