datasnoopingiin

Data snooping, also known as data dredging or p-hacking, refers to the practice of analyzing data in various ways until a statistically significant result is found, often without a prior hypothesis. This can occur unintentionally due to an exploratory approach to data analysis or intentionally to support a desired conclusion.

The core issue with data snooping is that it inflates the probability of finding a false positive. When many different tests or analyses are performed, the chance of at least one yielding a statistically significant result purely by random variation increases. This can lead to spurious correlations being mistaken for genuine relationships.

Researchers may engage in data snooping by repeatedly testing different subgroups, variables, or model specifications. This can be subtle, as they might not be consciously trying to mislead. However, the resulting "significant" findings lack the rigor of hypotheses tested prospectively.

To mitigate data snooping, researchers are encouraged to pre-register their study protocols and analysis plans. This means specifying hypotheses and statistical methods before data collection or initial analysis. If exploratory analysis is conducted, the findings should be treated as preliminary and require independent replication with a pre-defined analysis plan. Transparency about the analytical process is crucial in preventing and identifying data snooping.