The numbers don't tell anything by themselves

For instance, you can take an arbitrary set of random data and build a model (say, a polynomial function of high enough order) that would technically give you a 100% accuracy, at least as far as only the sample data points are considered. Then, on this account, you could go on to claim that the distribution is not random (following your logic here). But it would be a far cry from actually explaining anything in a meaningful way, let alone having any predictive power (the analogy of a random number generator seems appropriate here)

This is probably a borderline if not outright extreme example but it is still illustrative enough to show that numbers can be misleading as well as deceiving, and thus shouldn't be taken in isolation for drawing deductions and making inferences. You can arrive at very bizarre conclusions if you don't assess and keep in mind the nature of the subject matter. Put differently, statistical metrics are sufficient and unreservedly valid only in reference to truly random populations. With everything else, we should rather be concerned with looking for the underlying principles, forces, and causes, i.e. the actual mechanics of some phenomenon or process, and how things work internally

Mistaking correlation for causation has already become a cliché of sorts, but it is only a tip of the iceberg of the whole gamut of possible judgment errors we have to deal with and be aware of