How do you identify a quartile in a data set?

To identify a quartile in a data set, the process involves arranging the dataset in ascending order and then dividing it into four equal parts. This is crucial because quartiles are specific points in a dataset that separate it into four sections, each containing 25% of the data.

The first quartile (Q1) is the value that separates the lowest 25% of the data from the rest, the second quartile (Q2), which is also the median, divides the data into two equal halves, and the third quartile (Q3) separates the lowest 75% from the highest 25%. This method provides valuable insight into the distribution and spread of the data, enabling comparisons and analyses.

Averaging the outer values of the dataset does not accurately represent the data's distribution and does not provide a specific quartile. Selecting the median is only related to finding the second quartile and does not address the identification of the other quartiles. Finding the mode identifies the most frequently occurring value in the dataset, but it does not provide insights into the quartile distribution. Each of these methods serves different statistical purposes but does not fulfill the requirement of identifying quartiles effectively.