[pdonis]

Independent reproduction of a successful prediction by a proposed scientific model would be a first step towards a predictive track record, yes. But only a first step. A solid predictive track record for a model has to be built up over a substantial period of time and a substantial number of accurate predictions, not just one trial and one replication.

Note, also, that in many cases trials that produce statistically significant results, even when replicated, are not testing any actual model. They are just testing a hypothesis about a correlation. Correlation is not causation, and most such trials are not even capable of testing an actual causal model--they can test for the presence of a correlation, but not why it is present. Figuring out the actual causal process and developing a predictive model based on it takes more follow-up work. So really most such trials are just a first step towards developing a predictive model at all--but even after such a model is developed, you then have to take the time to assemble a good predictive track record for the model.

[andi999]

I agree, but am fine with less. I think no mechanism for head pain reliev is known, but we know that some medication works (painkiller). Ultimately we need to know why, but currently we have to life with less.

[pdonis]

> no mechanism for head pain reliev is known, but we know that some medication works

Yes; I would not consider this as evidence in favor of any scientific model, since, as you note, we don't have a scientific model that predicts what drugs should relieve headache pain. It's just an empirical generalization; as an empirical generalization, yes, it has a solid track record of accuracy behind it, but only as an empirical generalization, not as support for any scientific model. Of course we humans have lots of empirical generalizations we use all the time that are similarly not based on any scientific model. I am certainly not claiming that we should not make use of such generalizations when we have them. We just shouldn't confuse them with scientific models.