> It is a matter of keeping constants up to date in light of new information.
I'd argue it's a matter of calculating confidence intervals for all of your fitted parameters and displaying joint confidence intervals if they display a large degree of interdependence/nonlinearity, as well as showing the fitted residuals beneath the main plot so it's easily apparent what issues the fitted function might have, such as high leverage, or overfitting. The prediction made will be from the best-fit parameters, but the contributions of other possible models can be used to infer a distribution of potential outcomes. A parameter sensitivity analysis wouldn't hurt either.
Then you'll immediately know if you're looking at (or about to publish) junk.
I'd argue it's a matter of calculating confidence intervals for all of your fitted parameters and displaying joint confidence intervals if they display a large degree of interdependence/nonlinearity, as well as showing the fitted residuals beneath the main plot so it's easily apparent what issues the fitted function might have, such as high leverage, or overfitting. The prediction made will be from the best-fit parameters, but the contributions of other possible models can be used to infer a distribution of potential outcomes. A parameter sensitivity analysis wouldn't hurt either.
Then you'll immediately know if you're looking at (or about to publish) junk.