What happens when many strategies are tested on the same data without proper controls?

When many strategies are tested on the same data without proper controls, it increases the odds of a false positive. This phenomenon is commonly known as "data mining" or "overfitting." In this scenario, if numerous strategies are evaluated on the same dataset, there's a higher likelihood that some of those strategies will appear to perform well purely by chance, rather than due to any genuine predictive power.

This occurs because the more tests you run, the higher the probability of finding a statistically significant result that does not actually reflect the true underlying relationship. Essentially, if one tests a variety of models or strategies on limited data, it becomes increasingly difficult to distinguish between those that truly work and those that seem to work merely by coincidence.

The implication is that without appropriate controls, such as adjusting for multiple comparisons and using robust validation techniques, the findings can lead to misguided confidence in strategies that are simply relics of random data variations. Thus, maintaining rigorous controls in testing is crucial for achieving accurate and reliable results in strategy development.