Expected frequencies play a crucial role in assessing which of the following?

Expected frequencies are crucial in analyzing nominal data distributions, particularly in the context of chi-square tests. In these tests, researchers compare the observed frequencies of occurrences in different categories to what would be expected under the assumption of no association between the variables. The expected frequencies serve as a baseline for this comparison, allowing for the evaluation of whether any observed differences are statistically significant or if they could simply be due to random chance.

In contrast, the other options pertain to data types or analyses where expected frequencies do not have the same importance. For instance, interval data trends and continuous data means involve different statistical approaches, such as regression or t-tests, which focus on measures of central tendency rather than categorical distributions. Similarly, ordinal data rankings, while they can sometimes be analyzed using non-parametric tests, do not typically require an assessment of expected frequencies in the same way that nominal data does. Thus, the role of expected frequencies is primarily associated with nominal data distributions.