What is the most appropriate statistical technique to compare mean blood pressure between the treatment groups in a clinical trial?

The two-sample t-test is the most appropriate statistical technique for comparing mean blood pressure between treatment groups in a clinical trial when the assumptions of normality and equal variance are met. This test specifically evaluates whether there is a significant difference in the means of two independent groups, making it ideal for situations where you have continuous outcomes, such as blood pressure measurements, and want to compare them between different treatment conditions.

In clinical trials, it is common to measure continuous variables like blood pressure, and the two-sample t-test is particularly useful when dealing with two groups, allowing researchers to conclude if the observed differences in means are statistically significant.

This method assumes that the data from each group are normally distributed and that the variances of the two groups are equal. If these assumptions hold true, the two-sample t-test provides a powerful way to determine whether the treatment effect is significant.

Other methods, such as the Mann-Whitney U test, may be more appropriate for data that do not meet these assumptions (e.g., non-normally distributed data), and regression analysis is typically used for modeling the relationship between variables rather than simple group comparisons. The chi-squared test is suitable for categorical data, which blood pressure measurements are not. Hence,