본문/내용
Sequential vs. Factorial
With one-at-a-time experimental design, one may assess the main effect (Sum of Squares in factor A + SS in factor B, or variation arising from changing rows or columns individually in a-by-b data matrix) of each factor, but this design cannot assess interaction (SS in factor AB, or variation arising from changing rows and columns simultaneously) between each factor. Therefore, if factors interact, the sequential experimental design could lead to incorrect conclusions. I