Hypothesis Testing vs. Confidence Intervals in Healthcare Research
Hypothesis Testing vs. Confidence Intervals in Healthcare Research – HLT 362 Applied Statistics for Health Care Professionals
Hypothesis testing and confidence intervals are well-established procedures of inferential statistics that have significant use in healthcare research. Hypothesis testing is applied to decide whether, on the basis of a sample, there is adequate evidence in favor of a particular hypothesis about the population parameter (Wilson et al., 2024). This is done through forming a null hypothesis, which we expect to have no effect or difference, and an alternative hypothesis, which expects an effect or difference. The relevant null hypothesis is then compared to an identified population using t-tests or ANOVA as a method of determining whether or not to reject the null hypothesis. For example, hypothesis testing can be used by a hospital to determine whether the administration of a new drug has a statistically significant lower blood pressure than an existing drug.
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On the other hand, the confidence intervals give an estimate of the true population parameter within which it is believed to exist with a certain percentage of degree of confidence, for instance, 95%. The results from confidence intervals are generally more informative than hypothesis testing results since they provide the direction of the effect and the magnitude of the effect. For example, hypothesis testing may show that a new telemedicine approach has a p-value of 0.03 for a reduction in hospital readmission rates, but confidence intervals can tell how much the rate has reduced by, say, 10%.
In healthcare, these statistical methods give evidence to the practices carried out. For example, hypothesis testing is used in such institutions as the Mayo Clinic to confirm the effectiveness of new cancer treatments while the confidence intervals are used to improve cancer treatment procedures (Renjith et al., 2021). All these tools assist in decision-making among the healthcare providers, hence enhancing patients’ well-being and utilization of limited resources.
References
Wilson, K. J., Roldán-Nofuentes, J. A., & Henrion, M. Y. (2024). testCompareR: an R package to compare two binary diagnostic tests using paired data. Wellcome Open Research, 9, 351. https://pmc.ncbi.nlm.nih.gov/articles/PMC11535492/
Renjith, V., Yesodharan, R., Noronha, J. A., Ladd, E., & George, A. (2021). Qualitative methods in health care research. International journal of preventive medicine, 12(1), 20. https://journals.lww.com/ijom/fulltext/2021/12000/qualitative_methods_in_health_care_research.20.aspx
Hypothesis Testing vs. Confidence Intervals in Healthcare Research – HLT 362 Applied Statistics for Health Care Professionals
