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DOE is generally meant for continuous response data. Continuous data can be interpreted very easily as it can be, in most cases, fit into a particular probability distribution and insights can be drawn very easily. Also, the measurement of interactions of the different levels of inputs on the response can be very easily assessed. However, discrete DOE would be a difficult to handle as the response to the inputs needs to be fit into binary, ordinal or nominal categories. While the output can be fit into distributions like Poisson or Binomial, there is a chance that the result might be misinterpreted on account of limited number of trials. The resolution is not well captured in discrete output as good as it is can be done with continuous data. Despite these challenges, discrete data DOE can be a powerful tool in certain situations. For example, in quality control, we may want to investigate the factors that influence the probability of a product being defective. Or, in marketing, we might be interested in modeling the likelihood of a customer responding to a particular promotion.