In statistical reporting, what aspect of the variables is ignored when reporting chi square results?

When reporting chi-square results, the aspect of the variables that is often ignored is the expected counts. Expected counts are important in the context of chi-square tests because they provide a foundation for comparison with the observed counts. The chi-square statistic itself is calculated based on how the observed counts diverge from these expected counts. However, in many standard reporting practices, researchers may focus on the chi-square value, the p-value, and potentially the degrees of freedom, while not giving detailed attention to the expected counts.

It's essential to recognize that the expected counts play a crucial role in assessing the validity of the chi-square test. If the expected counts are too low, it can affect the robustness of the chi-square results, and thus, reporting them is necessary for a complete understanding of the analysis. Nonetheless, they can sometimes be overlooked in summaries or brief reports, leading to incomplete interpretations of the results.