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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.

Usually when the response variable in DOE is continuous it helps with most significant source of data which would be useful to analyze statistical method that in turn gives detailed data of the relationships between factors and its outcomes. However DOE can be executed with discrete data with some amount of risks involved in it. Here are some risks involved stated below- 1. Specific outcomes - Discrete data has limited levels which doesn't go down the level(nitty gritty) of the data, due to this it becomes difficult to find the most impacting effects 2. Analysis is not smooth - if there multiple factors with multiple levels the data cannot be interpreted easily 3. Lack of correct predictability - Since the data do not include or capture the full range of possible variations the chances of giving correct prediction is less In line with the above explanation it can be used for market survey, quality testing of data in a process etc. To conclude the DOE with discrete data does faces some significant challenges however with the proper planning and correct usage of statistical techniques are very much important to implement DOE with discrete data.