Well, nothing describes how "all science works", but ideally you don't revise your model based on new data. Instead, you take all of the data you have, both old and new, start over from scratch and try to find the simplest model that would account for it. What your old favorite model used to be, before you obtained the new data, should not play any role in your evaluation of competing models.
Given how real people and organizations work, psychology, resource limitations, incentives, etc., this is VERY idealistic, but still, imagine that you had found your data in a different order, so you ended up with the same data you have now, but had a different subset previously. With two different subsets in the past, your best past models might have been different from each other, but your best choice now should be the same now that both paths have converged on the same data. So why should your choice in the past have a "vote" in your choice now?
So (again, ideally), don't "revise" your old model; take the data you have now, old and new, pretend you are starting from scratch, and choose the best model. If a theory such as String Theory keeps failing to account for new data and is repeatedly modified to keep it from being disqualified, a reasonable question would be whether, if we started from scratch knowing what we know now, we would come up with this repeatedly patched String Theory version as our first choice.
(And, yes, I know that from a Bayesian perspective you can't literally start "from scratch", but that doesn't mean you have to use your most recent model as your prior).
Given how real people and organizations work, psychology, resource limitations, incentives, etc., this is VERY idealistic, but still, imagine that you had found your data in a different order, so you ended up with the same data you have now, but had a different subset previously. With two different subsets in the past, your best past models might have been different from each other, but your best choice now should be the same now that both paths have converged on the same data. So why should your choice in the past have a "vote" in your choice now?
So (again, ideally), don't "revise" your old model; take the data you have now, old and new, pretend you are starting from scratch, and choose the best model. If a theory such as String Theory keeps failing to account for new data and is repeatedly modified to keep it from being disqualified, a reasonable question would be whether, if we started from scratch knowing what we know now, we would come up with this repeatedly patched String Theory version as our first choice.
(And, yes, I know that from a Bayesian perspective you can't literally start "from scratch", but that doesn't mean you have to use your most recent model as your prior).