Question

Researchers recorded data on the amount of sleep reported each night during a week by a random sample of 20 high school students. Here are parallel boxplots comparing the distribution of time slept on all 7 nights of the study: Which outlier stands out the most? Why?

Answer 1

The outlier that stands out the most in the parallel boxplots comparing the distribution of time slept on all 7 nights of the study can be identified by observing the data point that lies farthest from the rest of the boxplot.

Step-by-step explanation:

The outlier that stands out the most in the parallel boxplots comparing the distribution of time slept on all 7 nights of the study is the data point that is significantly different from the rest of the data. To determine which outlier stands out the most, we can look for the data point that lies farthest from the rest of the boxplot. This can be identified by observing the horizontal lines, or whiskers, of the boxplot.

For example, if there is a data point that lies outside the whiskers and is visually separated from the rest of the data, it can be considered as the outlier that stands out the most. Outliers can be caused by various factors such as measurement error, data entry error, or extreme values within the dataset (outlier, parallel boxplots, distribution).