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P Value

There is a definition of the P-value in every statistical textbook. Also there is a controversy in one way or another and it has been going on at least for the last 20 years. First I’m going to tell you what a P-value is not. A P-value is not the probability that your theory false. It is not the probability that the null hypothesis is true. It has nothing to do, really, with your hypothesis. It is a sign of how well you’ve done your research, major determinants of the P-value. The P-value being a statistic that you compute from the data in your study. The main influence on the P-value first of all, is the sample size. Second, the reliability of the measures that you use, the quality of your research design, the choice of analysis, you make. The fidelity with which you actually execute your research design, and how well you execute your analysis in the end. So, it all primarily has to do with the quality of the research.

To learn more from Dr. Helena Kraemer listen to the podcast episode below.

P Value Part 1

P Value Part 2

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    Next-Generation Artificial Intelligence for Diagnosis: From Predicting Diagnostic Labels to "Wayfinding"

    Julia Adler-Milstein, PhD1; Jonathan H. Chen, MD, PhD; Gurpreet Dhaliwal, MD

    Improving the diagnostic process is a quality and safety priority.With the digitization of health records and rapid expansion of health data, the cognitive demand on the diagnostician has increased. The use of artificial intelligence (AI) to assist human cognition has the potential to reduce this demand and associated diagnostic errors. However, current AI tools have not realized this potential, due in part to the long-standing focus of these tools on predicting final diagnostic labels instead of helping clinicians navigate the dynamic refinement process of diagnosis. This Viewpoint highlights the importance of shifting the role of diagnostic AI from predicting labels to “wayfinding” (interpreting context and providing cues that guide the diagnostician).