Introduction
In nursing, there is a necessity to prove or, in other words, to validate research findings. The purpose of this method is to ensure that the results obtained and the recommendations provided are credible and they are to be used as references to multiple evidence-based practices. The most common method of data validation is hypothesis testing (Shreffler J, & Huecker, 2021). Alternative ones are sample surveys and a test under controlled conditions. This essay will examine how hypothesis testing and two alternate methods of data validation support the field of nursing.
Examination of the Usefulness Hypothesis Testing and Alternate Ways of Data Validation
To begin with, hypothesis testing is the method of assessing the sample data. One statement is to be proved, and the other one to be rejected. Evidence that either confirms or rejects the initial statement’s validity can be found by measuring and examining a set of data (Emmert-Streib & Dehmer, 2019). From the perspective of the usefulness of this method for nursing practice, a variety of medicine-related research require to be validated before they can be used for evidence-based practices implementations.
Other methods to validate data are tests under controlled conditions and surveys. The first method can create a model that would undergo the same stages of testing as the one used in the research being examined. It helps prove that the results claimed to be received are viable. Surveys question people who belong to the population affected, depending on the validated research (Dutta, 2020). The answers and the statistical analysis can either confirm or reject the research outcomes.
Conclusion
Therefore, hypothesis testing is a valuable method for validating the results of the research that can be used as references for evidence-based practices. Besides hypothesis-testing, there are two alternative ways to confirm that outcomes are trustworthy. Tests under controlled conditions and surveys are additional methods to validate that a study’s results can be used in medical practice further. Not any medical activity can be done without a previous literature review, and that’s why validation is crucial.
References
Dutta, S., Wei, D., Yueksel, H., Chen, P. Y., Liu, S., & Varshney, K. (2020, November). Is there a trade-off between fairness and accuracy? A perspective using mismatched hypothesis testing. In International Conference on Machine Learning (pp. 2803-2813). PMLR. Web.
Emmert-Streib, F., & Dehmer, M. (2019). Understanding statistical hypothesis testing: The logic of statistical inference. Machine Learning and Knowledge Extraction, 1(3), 945-961. Web.
Shreffler J, & Huecker MR. (2021). Hypothesis testing, P values, confidence intervals, and significance. StatPearls Publishing. Web.