Reproducibility and replicability are crucial components of the scientific method, but they may be compromised when there are inherent issues related to a study and analytic choices such as statistical errors or misalignments between the study’s objectives and implementation. Indeed, statistical errors and misunderstandings contribute to low reproducibility and replicability, hindering independent verification or changes in the direction of research (McNutt, 2014). Such problems can easily occur in health science, where there are many confounding factors and low prior odds of genuine findings (Ioannidis, 2005).

Reproducibility and replicability are crucial components of the scientific method, but they may be compromised when there are inherent issues related to a study and analytic choices such as statistical errors or misalignments between the study’s objectives and implementation. Indeed, statistical errors and misunderstandings contribute to low reproducibility and replicability, hindering independent verification or changes in the direction of research (McNutt, 2014). Such problems can easily occur in health science, where there are many confounding factors and low prior odds of genuine findings (Ioannidis, 2005).

Reproducibility and replicability are crucial components of the scientific method, but they may be compromised when there are inherent issues related to a study and analytic choices such as statistical errors or misalignments between the study’s objectives and implementation. Indeed, statistical errors and misunderstandings contribute to low reproducibility and replicability, hindering independent verification or changes in the direction of research (McNutt, 2014). Such problems can easily occur in health science, where there are many confounding factors and low prior odds of genuine findings (Ioannidis, 2005).