Hypothesis Testing
Hypothesis testing and confidence intervals are utilized in arrive at conclusions regarding a population by evaluating the specific population sample. Hypothesis testing is utilized to infer the finding of a hypothesis conducted on a sample data/representative data from a larger population. Therefore, hypothesis testing involves measurement and examination of a random sample of the population under investigation. In healthcare research, hypothesis testing is an assumption made regarding the population parameter and hence the confidence interval helps in rejection of the null hypothesis. Hypothesis Testing Essay paper
Generally, in healthcare, the research aims to establish correlations and answers in the data in order to facilitate the provision of better and improved patient outcomes. Correlation is not proof of causation. Clinical significance is what determines if the findings of the research can be practically applied to an individual or the general population. Clinical significance of research findings is also used in determining health care decisions (Greeland et al., 2016). The confidence interval is an interval estimate for the mean of the findings and therefore used to indicate the significance of the study findings by showing the reliability of the findings; a narrow confidence interval indicates high precision and credibility of the data while a wide confidence interval indicates lack of credibility of the data; therefore, confidence interval is useful in rejecting or accepting the hypothesis (Robert & Cumming, 2019).
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For example, when testing a hypothesis that homeopathic preparation is effective analgesia and reduces swelling post-oral surgery, the findings postoperative indicated that homeopathic preparation resulted in a mean decrease in swelling of 4 mm. The range of the 95% confidence interval was −4.5 to 8.5 mm. This wide confidence interval is suggestive that there was neither a reduction or an increase in swelling because of homeopathic preparation. This, therefore, leads to rejection of the hypothesis.
References
Greeland S, Senn S, Rothman K, Carlin J, Poole C, Goodman S & Altman D. (2016). Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur J Epidemiol, 31(1), 337–350.
Robert C & Cumming G. (2019). Estimation for Better Inference in Neuroscience. eNeuro, 6(4), ENEURO.0205-19.2019. Hypothesis Testing Essay paper