The concept of expected frequency is primarily associated with which type of data?

The concept of expected frequency is primarily associated with categorical data because it relates to the number of occurrences we expect in each category when conducting statistical tests, such as the chi-square test. In these tests, the expected frequency is calculated based on the distribution of data across distinct categories or groups.

In categorical data, the variables represent labels or groups rather than numeric values, making expected frequencies particularly relevant when testing hypotheses about the proportions or distributions of these categories. For instance, if you were to test whether a die is fair, you would expect each side of the die to come up an equal number of times over many rolls, leading to specific expected frequencies for each outcome.

Quantitative data pertains to numerical values and can be further divided into discrete and continuous types, but expected frequency isn’t typically calculated in the same way for these data types. Continuous data involves measurements that can take an infinite number of values within a given range, which also doesn’t align with the concept of expected frequency as it does not fit into distinct categories. As such, categorical data is the correct association for the concept of expected frequency.