What is the purpose of using quartiles in statistical analysis?

Using quartiles in statistical analysis serves the purpose of dividing the dataset into equal parts. Quartiles, specifically, split the data into four equal sections, allowing for a clear understanding of the distribution of values within the dataset. This division helps in identifying the variability and spread of the data by providing insights on how the data points relate to each other and where they fall within the overall dataset.

For example, the first quartile (Q1) represents the 25th percentile, meaning that 25% of the data points lie below this value. The second quartile (Q2) is the median, or the 50th percentile, and the third quartile (Q3) marks the 75th percentile. By examining these quartiles, analysts can better understand the data's skewness and identify outliers, enhancing the interpretative power of statistical results.

In contrast, the other options relate to different aspects of data analysis: calculating the mean deals with central tendency but does not provide the same distribution insight, identifying the mode focuses on the most frequently occurring value, and measuring central tendency can be achieved through various means, but does not specifically involve the quartile method. Each of these elements serves unique roles in statistical work, but the quartile