Reading focus
Syllabus: What Can Individual Differences Predict?
Follow the separation between developing a model and evaluating it on new observations.
Why you’re reading it. Establish the difference between a plausible explanation, a good fit, and a useful forecast.
A question to consider. The programme chooses its model after comparing results on the same group of former participants. How would you design an evaluation that this model has not already influenced?
Why this reading
A model can fit the observations used to build it and still predict new observations poorly. This methodological argument makes that gap central and asks psychologists to evaluate predictive performance directly.
Before you read
Training data are used to develop a model; test data assess it on observations withheld from development. Overfitting occurs when a model learns sample-specific patterns that do not generalize. Cross-validation rotates which observations are withheld. Statistical bias in the bias–variance discussion is different from unfair treatment of social groups.
Read for
- Distinguish accounting for variation in an existing dataset from predicting an outcome in new data.
- Follow why preprocessing, choosing variables, and tuning a model must also respect the separation between development and evaluation.
- Examine the argument for prioritizing prediction and identify what a successful prediction would still leave unexplained.
Consider afterwards
- A researcher tries many models and reports the one that performs best on a supposed test set. What has that test set become, and what further evidence would establish performance on new cases?
Editorial context
The title advances a position about research priorities, not a demonstration that causal explanation is unnecessary. The authors retain a role for causal research. Predictive success does not by itself establish a mechanism or an intervention effect. The article’s scientific examples reflect 2017; the transferable contribution is its argument about model evaluation, not a current verdict on every example.