Could this result just be ordinary luck?
Start with a simple model. Imagine two directions are equally likely, like a fair coin. We then observe one direction 39 times in 60 non-zero examples. A p-value asks how often a split at least this unusual would appear if that fifty-fifty model generated repeated samples.
If the answer is small, the observed split sits awkwardly with that narrow model. It may be evidence that something more than ordinary random variation is happening. The calculation is conditional on the model, the test and the way the data were gathered.
What the number does not mean
A p-value of 0.02 does not mean there is a 2% chance that the result is luck or a 98% chance that the hypothesis is true. It also does not say that the effect is large, useful, profitable or likely to repeat. Those are different questions.
The p-value can be small because the sample is large enough to detect a tiny difference. It can be misleading when assumptions are poor, observations are dependent or the chosen result was selected from many tests after looking at the data.
The search around the result matters
If one test is run, an unusual result is notable. If hundreds of variations are tried, some small p-values are expected by chance. Reporting only the winner hides that wider search. This is the multiple-testing problem.
Tradour therefore keeps the hypothesis family and exploratory status visible. The Entertainment result, for example, came from a broader feature search. Its unadjusted p-value describes a calculation; it is not a pass that promotes the pattern into the Playbook.