How to write an essay on Applied Statistics for Health Care (Solved): Logic of hypothesis testing

Hypothesis testing is the process of validating or invalidating a statistical test claim (Davis & Mukamal, 2022). It offers a framework for initiating determinations linked to population. The claim is typically linked to the studied population. Each hypothesis test has two contrasting statements(hypotheses) about the study group or population. The initial hypothesis is the null hypothesis stating that the parameter used for testing is the same or equal to the claimed value. The second set of hypotheses is an alternative hypothesis applicable when the null hypothesis is rejected or untrue (Zhao et al.,2017). Alternatively, the null hypothesis is the stand one takes before the claim is proved, while the alternative hypothesis is the stand one can take if the evidence is enough.

This quantitative research process is essential when coming up with adjustments and changes. Evidence must support the need for adjustment or change in interventions and practices in a clinical setup. The evidence is provided by conducting research and testing both hypotheses.

An example in a clinical setting would be, I think drug A is better than drug B in treating condition X. I will then use data to support this. I will determine if is true by randomly collecting the efficiency and effectiveness of using both drugs A and B in the treatment of condition X. If I lack enough evidence to support drug A is better than B in management of condition X,  I will fail to reject the null hypothesis, and I will conclude drug A is better than B in management of condition X. If the findings were sufficient to reject null hypothesis then the alternative hypothesis would be drug B is better than drug A in management of disease X.

References

Davis, R., & Mukamal, K. (2022). Hypothesis Testing. Circulation. Retrieved 2 January 2022, from https://www.ahajournals.org/doi/full/10.1161/circulationaha.105.586461.

Zhao, Z., De Stefani, L., Zgraggen, E., Binnig, C., Upfal, E., & Kraska, T. (2017, May). Controlling false discoveries during interactive data exploration. In Proceedings of the 2017 ACM International Conference on Management of Data (pp. 527-540). From https://doi.org/10.1145/3035918.3064019