Andmepüügikatsete

Andmepüügikatsete, often translated as data fishing expeditions or simply data fishing, refers to the practice of extensively searching through large datasets to find patterns, correlations, or insights without a pre-defined hypothesis. This exploratory approach contrasts with traditional hypothesis-driven research where specific questions guide the data analysis. In data fishing, researchers cast a wide net, examining various relationships and variables within the data, hoping to uncover something statistically significant or practically relevant.

The primary motivation behind data fishing is the desire to discover new knowledge or identify potential areas for further investigation. It can be particularly useful in fields with complex and voluminous data, such as genomics, finance, or social sciences, where initial hypotheses may not fully capture the intricate dynamics at play. The advent of powerful computational tools and the availability of "big data" have made data fishing increasingly feasible and prevalent.

However, data fishing carries a significant risk of spurious correlations. Because many relationships are tested, it is statistically probable that some will appear significant purely by chance, even if no true underlying relationship exists. This phenomenon is often referred to as p-hacking or the multiple comparisons problem. Consequently, findings from data fishing expeditions often require rigorous validation through independent datasets or controlled experiments before they can be considered reliable or accepted as scientific facts. Ethical considerations also arise, particularly when dealing with personal data, ensuring that the exploration does not lead to privacy violations or discriminatory outcomes.