Which of the following data-classification methods attempts to place an equal number of data values in each class?

Quantiles is a classification method designed to distribute an equal number of data values into each class. This approach sorts the data from lowest to highest and then divides it into a specified number of intervals or groupings, ensuring that each interval has the same number of data points. For instance, if the data set consists of 100 values and you choose to create four quantile classes, each class will contain 25 values. This method is particularly useful when aiming to highlight areas with a relative distribution of values, making it effective for visualizations that stress comparisons across equal segments of the dataset.

In contrast, Natural Breaks seeks to minimize variance within classes while maximizing variance between them, resulting in classes that do not necessarily have an equal number of data points. Equal Interval divides the range of data into equal width intervals, which can result in uneven distribution of values among classes, particularly in skewed datasets. The Standard Deviation method classifies data based on how much it deviates from the mean, which also does not ensure an equal distribution of data values across classes. Thus, quantiles stand out for their unique characteristic of balanced data distribution among classes.