![]() There are two schools of thought for what should be the error term to use for testing B and A*B. If the levels of factor A form the subplots, then the mean square for Block*A will be the error term for testing factor A. There is no single error term for testing all factor effects in a split-plot design. In this example, the plots under operator constitute the main plots and temperatures constitute the subplots. If the blocking factor is operator, observations will be made at different temperatures with each operator, but the temperature setting is held constant until the experiment is run for all material amounts. For example, suppose that factors are temperature and material amount, but it is difficult to change the temperature setting. This design is frequently used in industry when it is difficult to randomize the settings on machines. The block, which can be replicated, is termed the main plot and within these the smaller plots (variety strips in example) are called subplots. For example, in an agricultural experiment with the factors variety and harvest date, it may be easier to plant each variety in contiguous rows and to randomly assign the harvest dates to smaller sections of the rows. ![]() You might use this design when it is more difficult to randomize one of the factors compared to the other(s). A split-plot design is another blocking design, which you can use if you have two or more factors.
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