When is a contingency table most useful?

A contingency table is most useful when examining the relationship between two categorical variables. This is because a contingency table systematically displays the frequency distribution of variables, allowing researchers to easily observe how the two categorical variables interact with each other. For instance, one variable might represent gender (male or female), while the other might represent preference for a product (like or dislike). The table would show how many males and females like or dislike the product, facilitating analysis of any potential relationship.

In contrast, analyzing relationships in continuous data requires different statistical approaches or visualizations, such as scatter plots, which display correlations between numeric variables rather than frequency counts. Visualizing the distribution of scores typically involves graphical representations like histograms or bar charts, which focus on the distribution of scores within a single variable rather than the interaction between two categorical variables. Calculating confidence intervals is a statistical process associated with continuous data, where the goal is to estimate the range within which a population parameter is likely to fall. It does not pertain to categorical relationships that a contingency table provides insight into.