Clinical Inquiry and Hypothesis Testing Discussion Paper

Clinical Inquiry and Hypothesis Testing Discussion Paper

Clinical Inquiry and Hypothesis Testing Discussion Paper

Hypothesis testing is the process of making inferences on a particular parameter of study. The sample data or statistics could do this process. Others perform it using uncontrolled observational study (Haardörfer, 2019). The outcome of any test is to choose the most viable decision that would lead give a positive outcome on the clinical tests. The purpose of this assignment is to provide two different examples of how research uses hypothesis testing and describe the criteria for rejecting the null hypothesis. Again, the paper will discuss the importance of hypothesis testing in practice and with patient interactions.

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The first example of hypothesis testing can be a situation where two nursing teachers think that they use the best teaching methods for their nursing students. If each lecture has a statistic of 50 students enrolled in the nursing degree. Lecturer A has a class with students attending one lecture and one seminar each weak. On the other hand, Lecture B students only attend one lecture a week. Lecture A thinks that seminars in addition to class teachings are better teaching methods, while lecture B believes that attending the lecture classes is sufficient for the students. In this example, the lectures would want to know the best teaching method, which prompts both of them to examine the effect of two different teaching methods on student performance.

Another example could be a clinical researcher taking a sample of 500 breast cancer patients and testing a new drug designed to reduce this type of cancer. The statistical test done on this example would allow the researcher to approve or disapprove of the effect of the new drug in treating breast cancer.

These two examples would need both null and alternative hypotheses because the choice of a better option to solve the two problems lies in the choice of the null and alternative hypotheses (Mishra et al., 2019). For example, the hypothesis would be designed as follows,

Example 1

H_o: There is no significant difference in the teaching methods.

H¹: There is a significant difference in the teaching methods.

Example 2

H_o: There is no significant difference in breast cancer patients.  

H¹: There is a significant difference in breast cancer patients.

From the above examples, the null hypothesis will either be rejected or accepted in the two examples. If p< .05 and the alternative hypothesis is accepted in both cases, if all the tests were done at α = 0.05 level of significance. This signifies that there is enough evidence to conclude that the use of the teaching methods affects the students’ performance (Ho et al., 2019). Again, there would be enough evidence to prove that the new drug has an effect on breast cancer patients. However, if the output on the statistical summary will show p>.05, then the null hypothesis would be accepted. The conclusion made that the teaching methods or the use of the new drug do not significantly contribute to the performance of cure of breast cancer cancers respectively.

Hypothesis testing is important in nursing practice as it aids in strengthening the quality of the quantitative study. Besides, it aids in increasing the generalizability of findings by providing dependable knowledge that can be significant in the nursing practice (Mishra et al., 2019). The process is also important in making clinical decisions without relying on the level of significance given to research investigators.

Conclusion

Hypothesis testing and clinical inquiry affect the entire clinical study. This is because they expound on the findings and allows the researcher to prove the implication of the sample data in research. Besides, it acts as an effective decision-making point that changes the entire direction of research.

References

Haardörfer, R. (2019). Taking quantitative data analysis out of the positivist era: Calling for theory-driven data-informed analysis. Health Education & Behavior, 46(4), 537-540. https://doi.org/10.1177%2F1090198119853536

Ho, J., Tumkaya, T., Aryal, S., Choi, H., & Claridge-Chang, A. (2019). Moving beyond P values: data analysis with estimation graphics. Nature Methods, 16(7), 565-566. https://doi.org/10.1038/s41592-019-0470-3

Mishra, P., Pandey, C. M., Singh, U., Gupta, A., Sahu, C., & Keshri, A. (2019). Descriptive statistics and normality tests for statistical data. Annals of Cardiac Anaesthesia, 22(1), 67. https://dx.doi.org/10.4103%2Faca.ACA_157_18

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Provide two different examples of how research uses hypothesis testing, and describe the criteria for rejecting the null hypothesis. Discuss why this is important in your practice and with patient interactions.