What's Happening?
The scientific community is addressing the issue of inflated false-positive rates due to selective reporting of positive results. Researchers have traditionally focused on hidden comparisons, where selection occurs before results are published, leaving readers unaware. To combat this, practices like preregistration, data sharing, and reproducible computing have been adopted. However, a more prevalent issue remains unaddressed: the failure to adjust for multiple inferences in published work. This oversight can lead to a loss of statistical integrity, as only some results are corrected, ignoring the entire pool from which they were selected.
Why It's Important?
This development is significant as it highlights a critical flaw in scientific research practices that can affect the reliability of published findings. By not adjusting for multiple comparisons, the scientific community risks presenting skewed data, which can mislead further research and policy decisions. This issue impacts various stakeholders, including researchers, policymakers, and the public, who rely on accurate scientific data for informed decision-making. Addressing this problem could enhance the credibility of scientific publications and improve the overall quality of research outputs.
