What is the purpose of quartiles in data analysis?

The purpose of quartiles in data analysis is to divide a dataset into four equal parts. This statistical method helps in understanding the distribution of data by segmenting it. Quartiles provide insights into how data points are spread across the range, enabling analysts to identify the median (which is the second quartile), as well as the first and third quartiles that mark the boundaries of the lower and upper halves of the dataset, respectively.

This division is essential for various statistical analyses, as it allows for the assessment of variability and the detection of outliers. By calculating the quartiles, one can determine how data is concentrated and where most values lie, providing a clearer picture of the dataset's overall structure.

Other options, while related to data analysis, do not accurately describe the function of quartiles. For instance, identifying the mode is concerned with the most frequently occurring value, while calculating the average pertains to central tendency but does not segment data. Describing the shape of the data distribution is broader and does not specifically reference the function of quartiles, which are specifically focused on dividing the data into those four equal parts.