What is a key purpose of calculating quartiles in data analysis?

Calculating quartiles serves the key purpose of summarizing the spread and central tendency of the data. Quartiles divide a data set into four equal parts, providing valuable insights into the distribution of the data. Specifically, they help to identify the median (the second quartile), as well as the first and third quartiles, which highlight the range within which the middle 50% of the data lies.

By analyzing the quartiles, one can better understand not only where the central value is located but also how the data is dispersed. This information is crucial for understanding variability and can highlight potential outliers or the overall skewness of the distribution. Essentially, quartiles help in providing a clearer picture of the data set's characteristics beyond just the mean or median alone, making them an essential tool in data analysis.