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== Appendix==
== Appendix==
In the appendix, there are some scientific articles essential for understanding the topic on which questions can be generated at the end of the update course
'''[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7718515/ The Epistemological (Not Reproducibility) Crisis]'''
John Park
Department of Radiation Oncology, Kansas City VA Medical Center, Kansas City, Missouri
Department of Radiology, Univeristy of Missouri – Kansas City, Kansas City, Missouri
Corresponding author: john.park@va.gov
==Abstract==
The current reproducibility crisis is fundamentally a crisis of knowledge, thus in reality it is an epistemological crisis. The current reigning paradigm of null hypothesis testing using a P value of <.05 has made the medical literature prone to be filled with spurious correlations rather than true knowledge. This article brings attention to 3 foundational issues to help navigate the current crisis: The problem of induction, the concept of epistemological access, and the iatrogenics of information. Scientific reasoning is inductive reasoning and the problem of induction highlights the limitations of such knowledge. The concept of epistemological access is introduced to describe the inability of low-level data to extract true findings. This lack of true knowledge brings with it the iatrogenics of information, where having more data are in fact harmful and can lead to patients receiving ineffective treatments.
'''[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7315482/#CR7P-values%20–%20a%20chronic%20conundrum P-values – a chronic conundrum]'''
Jian Gao
Department of Veterans Affairs, Office of Productivity, Efficiency and Staffing (OPES, RAPID), Albany, USA
Jian Gao,
Email:Jian.Gao@va.gov
==Abstract==
In medical research and practice, the p-value is arguably the most often used statistic and yet it is widely misconstrued as the probability of the type I error, which comes with serious consequences. This misunderstanding can greatly affect the reproducibility in research, treatment selection in medical practice, and model specification in empirical analyses. By using plain language and concrete examples, this paper is intended to elucidate the p-value confusion from its root, to explicate the difference between significance and hypothesis testing, to illuminate the consequences of the confusion, and to present a viable alternative to the conventional p-value.
The confusion with p-values has plagued the research community and medical practitioners for decades. However, efforts to clarify it have been largely futile, in part, because intuitive yet mathematically rigorous educational materials are scarce. Additionally, the lack of a practical alternative to the p-value for guarding against randomness also plays a role. The p-value confusion is rooted in the misconception of significance and hypothesis testing. Most, including many statisticians, are unaware that p-values and significance testing formed by Fisher are incomparable to the hypothesis testing paradigm created by Neyman and Pearson. And most otherwise great statistics textbooks tend to cobble the two paradigms together and make no effort to elucidate the subtle but fundamental differences between them. The p-value is a practical tool gauging the “strength of evidence” against the null hypothesis. It informs investigators that a p-value of 0.001, for example, is stronger than 0.05. However, p-values produced in significance testing are not the probabilities of type I errors as commonly misconceived. For a p-value of 0.05, the chance a treatment does not work is not 5%; rather, it is at least 28.9%.
A long-overdue effort to understand p-values correctly is much needed. However, in medical research and practice, just banning significance testing and accepting uncertainty are not enough. Researchers, clinicians, and patients alike need to know the probability a treatment will or will not work. Thus, the calibrated p-values (the probability that a treatment does not work) should be reported in research papers.
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