Explanatory versus Predictive Modeling

Before beginning your analysis, you should use descriptive statistics to explore your data. After getting familiar with your data, you can use inferential statistics, or explanatory modeling, to describe your data. You can also use predictive modeling to make predictions about future observations. Let’s briefly compare explanatory and predictive modeling.

In explanatory modeling, the goal is to develop a model that answers the question, how is X related to Y? Sample sizes are typically small and include few variables. The focus is on the parameters of the model. To assess the model, you use p-values and confidence intervals.

The goal of predictive modeling is to answer the question, if you know X, can you predict Y? Sample sizes are typically quite large and include many predictor variables, also called input variables. The focus is on the predictions of observations, rather than the parameters of the model. To assess a predictive model, you validate predictions using holdout sample data.