The theory of evolution "appears to have successfully captured many of the patterns in the...data. This success [is] the main support for the theory"
[I've inserted the 'ToE' reference, and they go on to write]:
Why the Use of Good Fits as Evidence Is Wrong
This type of argument has three serious problems. First, what the theory predicts—how much it constrains the fitted data—is unclear. Theorists who use good fits as evidence seem to reason as follows: If our theory is correct, it will be able to fit the data; our theory fits the data; therefore it is more likely that our theory is correct. However, if a theory does not constrain possible outcomes, the fit is meaningless. [emp mine]
A prediction is a statement of what a theory does and does not allow. When a theory has adjustable parameters, a particular fit is only one example of what it allows. To know what a theory predicts for a particular measurement, one needs to know all of what it allows (what else it can fit) and all of what it does not allow (what it cannot fit). For example, suppose two measures are positively correlated, and it is shown that a certain theory can produce such a relation—that is, can fit the data. This does not show that the theory predicts the correlation. A theory predicts such a relation only if it cannot fit other possible relations between the two measures (zero correlation or negative correlation), and this is not shown by fitting a positive correlation.
When a theory does constrain possible outcomes, it is necessary to know by how much. The more constraint—the narrower the prediction—the more impressive a confirmation of the constraint (e.g., Meehl 1997). Without knowing how much a theory constrains possible outcomes, you cannot know how impressed to be when observation and theory are consistent.
...
That a theory fits data does not show how firmly the data rule out outcomes inconsistent with the theory; without this information, you cannot know how impressed to be that theory and observation are consistent.
Well, from this, I have a fair reason to be not very impressed at all with the theory of evolution!
Incidentally, the Meehl reference is a good read:
Meehl, P. E. (1997). The problem is epistemology, not statistics: Replace significance tests by confidence intervals and quantify accuracy of risky numerical predictions. In L. L. Harlow, S. A. Mulaik and J. H. Steiger (Eds.), What if there were no significance tests? (pp. 393-425). Mahwah, NK: Erlbuam.
