Prediction vs explanation

Prediction and explanation are related but different goals. A predictive model aims to estimate an unknown or future outcome accurately. An explanatory or mechanistic model aims to represent how biological processes generate observed behaviour.

Predictive success

A model can predict well using variables that are strongly associated with an outcome even when those variables are not themselves causal mechanisms.

Mechanistic understanding

A mechanistic model may be scientifically informative even when short-term prediction is limited by uncertain parameters, random events or unavailable future inputs.

Why distinguish them?

Model evaluation should match the goal. Forecasting requires out-of-sample predictive assessment; explanation requires scrutiny of assumptions, mechanisms and causal interpretation.