The aspect provided by Rumil Legaspi in his article, The Relationship between Hypothesis testing and confidence interval is essential for healthcare workers in making decisions. I understand that Hypothesis testing in research provides healthcare professionals with a basis for decision-making from tested data in a population parameter. Additionally, Legaspi clarifies that Confidence intervals provide a range of potential values and an approximation of exactness for the limit value in statistics (Frost, 2017). It was important for Legaspi to explain how these two concepts convene statistically and how they should be interpreted.
I agree with Legaspi’s point of view that these two concepts work together. I also concur with his remarkable argument that the two concepts are not often revisited to determine how they both work together. Consequently, I agree that confidence intervals and hypothesis testing provide a basis for reasoning depending on the sample pattern. I feel that with the author’s description confidence interval as a scale of possible values denoting an unknown limit in a particular extent of probability, it is possible to calculate a confidence interval (Legaspi, 2021). In relation, hypothesis testing provides a basis for concluding the credibility of the hypothesis from the sample tested (Frost, 2017). I understand the need for Legaspi to affirm that evidence from hypothesis testing is reliably applied to the larger population from which a sample was drawn.
I find it very helpful for Legaspi to blur out that the null hypothesis is the known facts and provides a basis for research to disapprove of its status quo. An alternative idea provides a basis for statistical significance or the claim that the hypothesis is expected to be true (Fleischmann & Vaughan, 2019). It is helpful for Legaspi to express the significance of P values, which help to determine whether professional healthcare providers can reject the null hypothesis (Fleischmann & Vaughan, 2019). In the writer’s view, it is clear that it is right to conclude from considerations of both confidence intervals and P values.
I agree with Legaspi that the aspect is correct in that the p values and the confidence intervals always correspond with each other. If hypothesis testing uses an alpha of 5%, giving results similar to a 95% confidence interval, then a correct conclusion is made in tandem with both concepts. I harmonize with Legaspi’s primary expression of hypothesis testing and confidence intervals as inferential techniques that can be used to validate a hypothesis.
References
Fleischmann, M., & Vaughan, B. (2019). Commentary: Statistical significance and clinical significance – A call to consider patient reported outcome measures, effect size, confidence interval and minimal clinically important difference (MCID). Journal of Bodywork and Movement Therapies, 23(4), 690–694.
Frost, J. (2017).How hypothesis tests work: Confidence intervals and confidence levels. Statistics by Jim.
Legaspi, R. (2021). The relationship between hypothesis testing and confidence intervals. Medium.