Which test is suitable for analyzing continuous data from three or more independent samples?

The most appropriate test for analyzing continuous data from three or more independent samples is the One-Way ANOVA. This statistical method evaluates whether there are significant differences between the means of the groups, which is essential when comparing multiple independent samples.

One-Way ANOVA is specifically designed for situations where there is one independent variable with three or more levels (groups) and a dependent variable that is continuous. It assesses the impact of the independent variable on the continuous outcome, allowing researchers to determine if at least one group mean differs from the others.

In scenarios where only two groups are being compared, other tests like the t-test are more suitable, and while Two-Way ANOVA also analyzes the effect of multiple independent variables on a continuous outcome, it is not applicable when only one factor is being examined.

The Chi-square Test is intended for categorical data, making it unsuitable for continuous data analysis. Similarly, the Mann Whitney U test is a non-parametric alternative for comparing two independent samples, which limits its application when dealing with three or more groups.

Choosing One-Way ANOVA enables researchers to efficiently analyze variations within their continuous data across multiple independent samples, thus providing valuable insights into their research questions.