The analysis of screening designs depends on effect sparsity, where most effects are assumed to be inactive. Using this assumption, effects with small estimates can help estimate the error in the model and determine whether the larger effects are active. Basically, if all the effects are inactive, they should vary randomly, with no effect deviating substantially from the other effects.
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For highly supersaturated main effect designs, the Screening platform is effective in selecting factors, but is not as effective at estimating the error or the significance. The Monte Carlo simulation to produce pvalues uses assumptions that are not valid for this case.
