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Blocking is a technique used to mathematically remove the variation caused by some identifiable change during the course of the experiment. For example, you may need to use two different raw material batches to complete the experiment, or the experiment may take place over the course of several shifts or days. For each of these cases, the change may cause the response data to shift. Blocking removes this shift and, in effect, “normalizes” the data.
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Flat Shoes Retro Ladies Brogues Leisure Women's Shoes Daily Heels Carving Leather Blue Dress Lace Up Single Fashion Juqilu Ankle Boots For example, in experiments with 16 runs, you may choose to carry out the experiment in 2 or 4 blocks. Two blocks might be helpful if, for some reason, you must do half the runs on one day and the other half the next day. In this case, day to day variation may be removed from the analysis by blocking.
When you choose to block your design, one or more effects will no longer be estimable. You can look at the alias structure to see which effects have been “lost to blocks.” This is especially important when you have 4 or more blocks. In certain cases, a two-factor interaction may be lost and so then you will want to make sure that the interaction is not one that you are interested in.
Another note about blocking - it is assumed that the block variable does not interact with the factors. The effect must only be a linear shift, and not be dependent on the level of one or more of the factors under study.
If you try to block on a factor, that factor will be aliased with the block and you will not get any statistical details on the effect of that factor. Only block on things that you are NOT interested in studying.
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On the other hand, if you want to study the effect of raw material batch variation, then it should be included as a factor and you should NOT set up blocks on this factor. Consider using a split-plot design instead of blocking in these cases.