Which test is frequently used in quality control to analyze categorical variables?

The Chi-square test is widely used in quality control for analyzing categorical variables because it assesses how expected and observed frequencies relate to each other. In quality control settings, data often consists of classifications or categories, such as defective items versus non-defective items, or the presence of certain attributes in products. The Chi-square test allows quality professionals to determine if there is a significant difference between observed frequencies in different categories and the frequencies that would be expected under a specific hypothesis. This capability makes it particularly useful for evaluating the performance of manufacturing processes, product quality, or customer satisfaction based on categorical data.

In contrast, the other options, while valuable in different contexts, are not designed for categorical variables. The T-test and ANOVA are used for comparing means and require continuous data, whereas Pearson correlation measures the strength and direction of a linear relationship between two continuous variables. Therefore, the Chi-square test stands out as the appropriate method for analyzing categorical variables in quality control scenarios.