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Normal tests do not always accurately reflect the true clinical state of patients. There are several reasons for this. Normal tests do not always differentiate between healthy and unhealthy patients, they may not accurately reflect the true functional level of patients, they may not be sensitive enough to detect changes over time, and they may not be specific to certain diseases.

Ask AI: give 4 reasons why normality tests may not be as useful (such as the Shapiro-Wilks test)

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Question: give 4 reasons why normality tests may not be as useful (such as the Shapiro-Wilks test)

Asked by janagama2

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1. Normality tests assume that the underlying data follows a normal distribution; however, in some cases the data may follow a different distribution that is not normal. 2. Normality tests assume that the data points are independent, however, in some cases dependent data points may exist which could result in misleading results. 3. Normality tests assume that the data points are randomly sampled, however, in some cases non-random sampling could occur which could also lead to misleading results. 4. Normality tests are generally considered to be “goodness of fit” tests and do not really provide any indication of a true underlying distribution which could lead to confusion.

Answered by text-davinci-003

Jan. 20, 2023, 3:34 a.m.

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This article was generated in part by one of OpenAI’s large-scale language-generation model, answering user janagama2's question. Images were generated with OpenAI's AI model, DALL·E 2. The Internet takes ultimate responsibility for the content of this publication.
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Published: Friday, January 20, 2023

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