In its simplest form, a scientific experiment aims at predicting the outcome by introducing a change of the preconditions, which is represented by one or more independent variables, also referred to as “input variables” or “predictor variables.” The change in one or more independent variables is generally hypothesized to result in a change in one or more dependent variables, also referred to as “output variables” or “response variables.” The experimental design may also identify control variables that must be held constant to prevent external factors from affecting the results. And to do good science with data, one needs to collect it through carefully thought-out experiments to cover all corner cases and reduce any possible bias. A well-planned DOE can give a researcher meaningful data set to act upon with the optimal number of experiments preserving critical resources.Īfter all, the essential aim of Data Science is to conduct the highest quality scientific investigation and modeling with real-world data. This exercise has become critical in this age of rapidly expanding the field of data science and associated statistical modeling and machine learning. Design of Experiment (DOE) is an important activity for any scientist, engineer, or statistician planning to conduct experimental analysis.