Which statistical test is typically used when comparing two separate groups?

The independent t-test is the appropriate statistical test used when comparing two separate groups because it is specifically designed to assess whether the means of two independent samples are statistically different from each other. This test assumes that the two groups are unrelated and that the data from each group follow a normal distribution.

When you have two distinct sets of data that correspond to different groups, the independent t-test allows researchers to determine if any observed differences in their means are significant or likely due to random chance. This method is widely used in biomedical engineering and other fields where researchers often seek to understand differences in outcomes between two populations, such as a treatment group versus a control group.

The other options serve different purposes: ANOVA is used for comparing means across three or more groups, the paired t-test is useful for comparing two related groups (like measurements taken from the same subjects before and after treatment), and the chi-square test is used for categorical data to assess how likely it is that an observed distribution is due to chance.