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The result of a well-done exploratory study is a hypothesis. That’s the formulation of the hypothesis. And it also results in having, giving you the information, you need to design a good study for that hypothesis. Questions like, what populations should I sample? How should I sample it? What measures should I take? How should…
Pilot study is the most problematic term, because there’s so many people who will tell you that any small, badly designed study that leads to no viable conclusions, that’s a pilot study. Any that’s not what a pilot study is. The word pilot is to pilot study, is the same as pilot in pilot boat….
Fidelity means in a research study that you do the study exactly as you plan to do the study. It’s really crucial when you’re planning a study to set up what your sampling criteria are, what population you want your results to apply to, what your inclusion, exclusion criteria are. Where are you going to…
If you’re doing exploration, if you’re doing hypothesis testing, you have to have an priori hypothesis. a hypothesis has a rationale and justification, often drawn from previous hypothesis-generating studies.You set up a plan, who are you going to sample? What are you going to measure, how you are going to measure it, how you’re going…
What do you do when you have a set of observations, you don’t know what the distribution is, but you have a certain question, for example I want to test between two populations and I don’t know what the distributions in either are? What you do is, if you have let’s say 10 observations in…
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….
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).
