Data vs Models
Data are records of what happened; models are compressed theories of why; neither replaces the other and confusing them causes error.
Two Kinds of Knowledge
Data are observations: measured, noisy, finite records of particular events. A model is a compact structure that explains or predicts data by encoding assumptions about how the world works. Data tell you what happened in the cases observed; a model tells you what to expect in cases you have not seen. Each answers a question the other cannot.
The Complementary Roles
- Data without a model is a pile of measurements with no reach beyond itself.
- A model without data is an untested hypothesis, however elegant.
- Data constrain and test models; models organize and extrapolate from data.
- Prediction beyond the data always relies on a model, stated or hidden.
Data-Driven and Model-Based Extremes
A purely data-driven method interpolates observed patterns and can be very accurate inside the observed range, but has no principled basis for extrapolation and inherits every bias in its data. A purely model-based method encodes theory and extrapolates on principle, but is only as good as its assumptions. Most strong scientific work combines them: theory constrains the model, data calibrate and test it.
When Confusion Causes Error
Trouble comes from mistaking one for the other: treating a model's output as if it were a measurement, or treating a limited dataset as if it were the whole truth. A simulation is not data; a curve fit is not a law. Keeping the distinction sharp is what lets you say honestly how far a result reaches.
At Kronos
Design numbers from the breeder and burner are model outputs from verified solvers, not measurements from built hardware, and are labeled as such. Where operating-device data exist, they are used to validate the models; where they do not, results remain conditional predictions rather than facts.