25: Prediction vs Explanation

Author

Affiliation

Smit, A. J.

University of the Western Cape

1 Introduction

This chapter serves as a synthesis, bringing together the concepts from the entire modeling block to address a fundamental question: is the goal of your model to explain relationships or to predict outcomes? The answer determines the appropriate modeling strategy, from variable selection to model evaluation.

2 Key Concepts

• When Coefficients Matter: Emphasizing that for explanatory models, the primary goal is to obtain unbiased and interpretable estimates of predictor effects.
• When Prediction is Sufficient: Discussing scenarios where predictive accuracy is the main goal, and the internal workings of the model are less important (the “black box” approach).
• Choosing Strategies: Integrating everything learned about collinearity, model selection, overfitting, and proxies to choose the right tools for the job based on the primary research goal.

This chapter is a placeholder. Content will be developed further.