In a single-factor analysis of variance, MST is the mean square for treatments and MSE is the mean square for error. The null hypothesis of equal population means is likely false if: $\begin{array}{|l|l|}
\hline \text { A. } & \text { MST is much larger than MSE. } \
\hline \text { B. } & \text { MST is much smaller than MSE. } \
\hline \text { C. } & \text { MST is equal to MSE. } \
\hline \text { D. } & \text { MST is zero. } \
\hline
\end{array}$

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The Answer of In a single-factor analysis of variance, MST is the mean square for treatments and MSE is the mean square for error. The null hypothesis of equal population means is likely false if: $\begin{array}{|l|l|}
\hline \text { A. } & \text { MST is much larger than MSE. } \
\hline \text { B. } & \text { MST is much smaller than MSE. } \
\hline \text { C. } & \text { MST is equal to MSE. } \
\hline \text { D. } & \text { MST is zero. } \
\hline
\end{array}$

In a Single-Factor Analysis of Variance, MST Is the Mean