Clinical Inquiry and Hypothesis Testing Assignment

Clinical Inquiry and Hypothesis Testing Assignment

Clinical Inquiry and Hypothesis Testing Assignment

Hypothesis testing and confidence interval are two important aspects of statistics. While other researchers think that these two aspects are different, hypothesis testing needs a confidence interval to determine the level of significance in a conducted test (Kruschke & Liddell, 2018). Confidence Interval (CI) is the range of possible values that is likely to capture an unidentified parameter with a certain degree of confidence or probability. Therefore, the purpose of this write-up is to evaluate and provide examples of how hypothesis testing and confidence intervals are used together in health care research.

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Confidence interval and hypothesis testing are both inferential techniques which is a common feature that they both share. These inferential techniques both use samples to estimate a population parameter or test the validity and the strength of the hypothesis (Cready et al., 2021). Tests on hypothesis are centered around the null hypothesized parameter, while the confidence intervals revolve around the estimate of the sample parameter. For instance, if the value from the null hypothesis is found within the selected confidence interval, then it would imply that there is a poor confidence interval, and the p-value would be higher than the hypothesized value. In most cases, the null hypothesis would adopt a value of 0 as the point of no difference (Kruschke & Liddell, 2018). This would imply that if a researcher finds 0 within the confidence interval, it would imply a high chance of finding no difference on the parameters tested. Therefore, if the null hypothesized value falls within the confidence interval, then the p-value would be greater than 5%. Conversely, if the value is falling outside the confidence interval, that ten p-values would be less than 5%.

In the clinical setting, research relying on clinical trials rely on the sample obtained from a particular population of interest. However, the results obtained in these trials would only show the estimate of something that would happen if a particular treatment that has been tested is given to a general population (Kruschke & Liddell, 2018). In this case, the confidence interval would be used to provide a range of significant values for the population parameter and offer a general idea on the precision of the measured treatment and its impact on the general population.

In a workplace setting, the confidence interval and hypothesis testing are important in measuring healthcare costs within an institution. In the measurement of healthcare cost, the management would want to know when the average cost of healthcare reflects the almost unavoidable existence of a few subjects (Kruschke & Liddell, 2018). This will prompt hypothesis testing comparing the means on the average cost and high costs, and the hypothesis would be looking into the comparison of the means. This comparison would require a confidence interval that is highly related to the probability derived from the t-test. The CI for the given mean estimates a range of values that is based on the used sample means and their variability. These measures would be important in measuring the true mean of the assigned population. In most cases, clinical trials use 95% CI, which implies that it gives an allowance of a 5% false-positive rate as a standard of accepting or rejecting the null hypothesis (Schober et al., 2018).

Conclusion

Both hypothesis testing and confidence interval are important in the healthcare setting. For instance, in a workplace setting, healthcare institutions would want to determine the effectiveness of treatment in reducing cost. These measures would effectively ascertain the decision to go ahead with the treatment or stop. CI and hypothesis testing are of imperative importance in conducting statistical tests in nursing and drawing conclusions representing the general population.

References

Cready, W. M., He, J., Lin, W., Shao, C., Wang, D., & Zhang, Y. (2021). Is there a confidence interval for that? A critical examination of null outcome reporting in accounting research. A Critical Examination of Null Outcome Reporting in Accounting Research (February 2, 2021). https://dx.doi.org/10.2139/ssrn.3131251

Kruschke, J. K., & Liddell, T. M. (2018). The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective. Psychonomic Bulletin & Review, 25(1), 178-206. https://doi.org/10.3758/s13423-016-1221-4

Schober, P., Bossers, S. M., & Schwarte, L. A. (2018). Statistical significance versus clinical importance of observed effect sizes: what do P values and confidence intervals really represent?. Anesthesia and Analgesia, 126(3), 1068. https://dx.doi.org/10.1213%2FANE.0000000000002798

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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.