Reading the evidence
In-sample testing evaluates an idea on data available for developing, selecting or tuning it. It shows fit to the workshop material, not independent validation.
Make it click
A pupil works through questions while seeing corrections and adjusts the method. Improvement matters, but the same questions cannot provide a fresh exam.
Now put that into markets
Researchers use a development sample to understand data, form features and choose rules. Performance there is expected to benefit from that attention.
Cross-validation can structure reuse within development but still belongs to the broader development process. Hyperparameter choices and human judgment count as tuning.
Document the sample boundary and every decision it informed. Label results as development evidence and reserve independent data before exploration.
Strong in-sample fit can be useful for mechanism building but may be entirely accidental. It should never be presented as future performance evidence.
Takeaway
