Whenever we collect data of a model with the intention of testing something, we are implicitly working with an experimental design. Experimental design refers to the setup that defines which experimental units are used, and how they are allocated to each treatment. For example, if we want to measure whether clients are more likely to buy a product after receiving a discount, we need to define which clients will be in the control or test group. Furthermore, we need to define how many of them will fall in each group. All these decisions will have implications regarding the effects and contrasts that we can estimate, and what the precision will be for each one. This is why experimental design has transcendental consequences for our ANOVA and regression models.

Understanding the underlying design for an experiment is of prime importance. The design type has consequences not only for the efficiency of the estimations, but also for how we should set up the model. The difference between nested and crossed designs is a good example of this, because both designs require a specific formulation.