This article covers Hypothesis Testing and Confidence Intervals in Healthcare Research.
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Course HLT-362V
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.
Solution
Hypothesis Testing and Confidence Intervals in Healthcare Research
Hypotheses testing and confidence intervals
Healthcare research provides essential information to fill existing gaps or solve an existing problem, such as aging. Evidence-based studies help healthcare researchers make decisions in various practice areas. Researchers form a hypothesis, which is a proposed explanation of the relationship that exists between two variables. The hypotheses guide research. Hypothesis tests are done on the assumption of the selected parameters. A hypothesis can be measured using p-values or confidence intervals (Shreffler & Huecker, 2020). For instance, when controlling the number of falls in the older population, a researcher would want to determine the effectiveness of method A over method B. The hypothesis for this relationship would be that method A reduces the number of older patient falls in a hospital significantly compared to method B. The tests will measure the number of falls when method A is used, and the number of falls when method B is used. Researchers always try to reject the null hypothesis, which shows no relationship between variables. In this case, the null hypothesis would be; there is no significant difference between methods A over method B in reducing the number of older patient falls in a hospital.
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The researcher can utilize confidence intervals (CI) to test the null hypothesis (Barr, 1969). In most cases, a confidence interval of 95% is used. Confidence intervals represent a range of values that researchers believe in capturing the unknown parameter with a particular confidence level. It is an estimation or probability that researchers’ parameters lie between a specific value range. In this case, we have an assumption of the existing relationship between the selected variables. Therefore, if the CI captures the null hypothesis value claims or the hypothesized parameter, the results are close enough to the real population mean. The researcher cannot reject the null hypothesis. But if the CI does not capture the hypothesized parameter, the results are not close enough to the population mean, and they can reject the null hypothesis (Shreffler & Huecker, 2020).
For the above example, a range of values or intervals can be all hospitals that either method A or B has been implemented. A practice example would be determining the risk of diabetes using the BMI range of 25 – 29.9. A BMI of below 18.5 shows a person is underweight. That of 18.5 – 24.9 shows an individual is expected. Overweight persons range from 25.0 – 29.9 BMI. Therefore, nurses apply the overweight range to determine the risk. The hypothesis would be; overweight individuals are at a greater risk of diabetes. The null hypothesis would be; being overweight does not increase the risk of diabetes. A confidence interval (95% CI, 25 – 29.9) is used to confirm or reject the null hypothesis.
References
Barr, D. R. (1969). Using confidence intervals to test hypotheses. Journal of Quality Technology, 1(4), 256-258.
Shreffler, J., & Huecker, M. R. (2020). Hypothesis Testing, P Values, Confidence Intervals, and Significance. In StatPearls [Internet]. StatPearls Publishing.
Question
Course HLT-362V
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.
Related FAQs
1. What is the difference between a hypothesis and a confidence interval?
Confidence intervals and hypothesis tests are similar in that they are both inferential methods that rely on an approximated sampling distribution. Confidence intervals use data from a sample to estimate a population parameter. Hypothesis tests use data from a sample to test a specified hypothesis.
2. Is there a role for confidence intervals in medical research?
Confidence intervals in medical research The utility of confidence intervals in a wide variety of situations in the medical field is re-emphasized, with examples drawn from controlled clinical trials, disease control programmes, vaccine trials and laboratory studies.
3. Is the 95% confidence interval a reasonable estimate of population mean?
Because 98.6 is not contained within the 95% confidence interval, it is not a reasonable estimate of the population mean. We should expect to have a p value less than 0.05 and to reject the null hypothesis. There is evidence that the population mean is different from 98.6 degrees.
4. Can a 95% confidence interval reject a null hypothesis?
In other words, if the the 95% confidence interval contains the hypothesized parameter, then a hypothesis test at the 0.05 α level will almost always fail to reject the null hypothesis. If the 95% confidence interval does not contain the hypothesize parameter, then a hypothesis test at the 0.05 α level will almost always reject the null hypothesis.