Models Are Tools, Not Truth
A model earns its keep by being useful under stated conditions — not by being right in general, which no model is.
Every model is wrong somewhere. That is not a failing to be apologised for; it is what a model is. A model is a deliberate simplification chosen so that something becomes computable, and the simplification is the whole point. The question is never whether a model is true. It is where it holds, how well, and how you would notice when it stopped.
State the conditions, not just the fit
A fit reported without its conditions is close to meaningless. The same estimator that behaves beautifully inside the range you sampled can produce confident nonsense a short distance outside it, and nothing in the fitted parameters will warn you.
m̂(x) = Σᵢ K((x − xᵢ)/h) · yᵢ ⁄ Σᵢ K((x − xᵢ)/h)
So the reporting standard we hold ourselves to is: the model, the data it was fitted on, the range over which it was checked, the residuals, and the conditions under which we expect it to fail. A model published without the last two is a claim, not a tool.
A model is a useful compression of reality, not reality itself.
There is a practical benefit to treating models this way. If a model is a tool rather than a belief, replacing it costs nothing but work. If it has become a position, replacing it costs face — and that cost is paid, eventually, by the accuracy of the result.