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Evaluate and provide examples of how hypothesis testing and confidence intervals are used together in health...

Evaluate and provide examples of how hypothesis testing and confidence intervals are used together in health care research. Provide a workplace example that illustrates your ideas.

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Hypothesis testing and estimation used to make conclusions about the population by examining the population. It is mostly used in medicine, dentistry, health care, biology and provide information to make decisions or conclusions. The hypothesis is a statement about one or more populations. Both Hypothesis testing confidence intervals focus on population or generalization, but in a different way, it provide different information.
Confidence intervals used in research data provide assurance about data usually calculated in percentage. This wide interval indicates more data should be collected before(conclusion) hypothesis testing done. It is a range of values contain the unknown population parameters. It serves good estimates of the population parameter.
Example:
Children vaccinated for polio is 2.5 times less to get polio. Other children are prone to polio. so 2.5 can be little or not. We received almost nearly correct answer in our sample. the confidence interval can provide the truth parameter of the 95% level.
The majority of the confidence level will be 95%. It reflects the truth. If the confidence interval is 100% truth then there will be an error 100%. more information we collect can have more truth because in polio vaccination a lot of children were involved. the confidence interval can measure the truth by how much the information was collected. when we did not collect the information or data or misdiagnosed it can affect the vaccination. So hypoyhesis testing in polio provides benefit and specific value of interest call null hypothesis(variation).
Hypothesis testing avoids null in the data and provides evidence and quantity data. Hypothesis testing based on calculating obtained extreme or more extreme than observed in the sample.
P-value or probability value has evidence against the null. With p-value, the null hypothesis looks quite reasonable. So when confidence interval provides 95% of the result null hypothesis tells about p-value if hypothesis test required or not.

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