Blocking
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.
Stat-Ease provides various options for blocking, depending on how many runs you choose to perform. The default of 1 block really means “no blocking.”
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.
Note
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.
Example: You are trying to determine the effects of factors in a coating process such as speed, temperature, and pressure on your product’s tensile and elongation properties. Due to the number of runs involved, you will need to use two different batches of raw material. You expect that variations in the raw material may have an effect on the response, but you are not interested in studying that effect at this time. Therefore, raw material is NOT a factor and you should block on it instead. This will remove the effect of raw material on tensile and elongation from the ANOVA and allow you to better identify the other factor effects.
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.