What is the focus of the data-snooping bias?

Data-snooping bias refers to the issue of overfitting a model or trading rule to a specific dataset by repeatedly testing various strategies until one that looks successful is found. This often involves using previously collected data to validate or create these trading rules. When researchers or traders apply this practice, there’s a risk of mistakenly attributing the success of a strategy to its inherent quality rather than the randomness or peculiarities of the historical data it was derived from.

This bias often leads to the misconception that a strategy or trading rule will perform adequately in future trading because it was successful during backtesting or historical analysis. As a result, it's important to recognize how the prior results from earlier studies can influence newly proposed strategies, often without adequate testing against new or out-of-sample data to verify their robustness. Thus, understanding data-snooping bias emphasizes caution in relying on past performance to justify current trading decisions.