HLT 362 Evaluate and provide examples of how hypothesis testing and confidence intervals are used together in health care research
HLT 362 Evaluate and provide examples of how hypothesis testing and confidence intervals are used together in health care research
Hypothesis testing and confidence intervals are used together in healthcare research. A hypothesis is a forecast statement of what will occur between two variables. The independent and dependent variables are identified in the hypothesis and analyzed with gathered data to show correlations and relationships between variables. A hypothesis is created when variables are identified.
A confidence interval (CI) is an interval estimate of the mean which is a range of values of the data. These values are close to the mean in a negative or positive direction. The CI shows the risks of being wrong. If the CI reduces the risk of error increases. A CI of 95% says that 5% of the mean will not be true yet 95% will be a true mean. (Ambrose, 2018)
A workplace example where the CI is used is a study suggesting that working shift work for long hours during pregnancy can be associated with adverse pregnancy risks. The study showed that working a fixed night shift measured a CI of 95% with increased odds of miscarrying when compared to standard working hours. The study also revealed that working rotating shifts revealed a CI of 95% of increasing odds for preterm delivery. (Cai et al., 2019) Using CIs with multiple variables, this study concluded that pregnant women increase risks of adverse pregnancy outcomes if working rotating shifts, fixed night shifts or longer hours.
I work in a female-dominated industry and department. I frequently see my pregnant colleagues being placed on alternative duty or light duty while pregnant. This study concerns me because the “light duty” does not decrease their hours but instead keeps them from working “on their feet” all shift. It would be interesting to see if these coworkers have increased adverse pregnancies in their work situations.
Ambrose, J. (2018). Applied Statistics for Health Care. Gcumedia.com. https://lc.gcumedia.com/hlt362v/applied-statistics-for-health-care/v1.1/#/chapter/3
Cai, C., Vandermeer, B., Khurana, R., Nerenberg, K., Featherstone, R., Sebastianski, M., & Davenport, M. H. (2019). The impact of occupational shift work and working hours during pregnancy on health outcomes: a systematic review and meta-analysis. American Journal of Obstetrics and Gynecology, 221(6). https://doi.org/10.1016/j.ajog.2019.06.051
Both confidence intervals and hypothesis tests are inferential methods that depend on a sample distribution that is approximated. Confidence intervals are used to measure a population parameter using data from a survey. Hypothesis experiments are used to test a hypothesis using data from a study. Hypothesis testing necessitates the presence of a parameter that has been hypothesized. A hypothesis test determines whether the outcome is exceptional, whether it is reasonable chance variation, or whether it is too extreme to be considered chance variation.
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In health-care science, hypothesis testing, and confidence intervals are used together. This is used as an interval estimation for the mean with confidence interval (CI). A confidence interval (CI) is a set of values that are like the mean and can affect the direction in either a positive or negative way. Means using a procedure that contains the population mean with a defined proportion of the time, usually 95 percent or 99 percent of the time, are given a confidence interval (CI). The CI is the range in which the researcher could be incorrect. A 95% confidence interval indicates that 95% of a research sample will contain the true mean, while the remaining 5% will not. Confidence intervals will help you compare the accuracy of various estimates with this in mind. For example, 95 percent of the data collected in a test survey of 100 participants will be correct, while five out of 100 will be incorrect. If the 95 percent is decreased, the chance of error increases (Ambrose, 2018). Since hypothesis testing and confidence intervals are used together in health care research, this is important to note.
If you wanted to know the mean of temperatures obtained in a hospital with COVID-19 patients, you’d need to think about hypothesis testing and confidence intervals. Since it’s necessary to have a true mean of the temperatures of the sample collected, a CI of 95 percent will be better than a CI of 90 percent for this example. This is because the CI is determined by first determining the sample size, then determining the mean and standard deviation, and finally determining the degree of confidence interval.
It’s crucial to understand analytical quantitative analysis, which requires hypothesis testing and confidence intervals, to produce reliable findings from samples for the populations being studied. This is particularly relevant in health care, where positive outcomes can be established to enhance patient care.
How hypothesis tests work: Confidence intervals and confidence levels. (2019, June 25). Statistics By Jim. https://statisticsbyjim.com/hypothesis-testing/hypothesis-tests-confidence-intervals-levels/
LibGuides: Maths: Hypothesis testing. (2020, May 13). LibGuides at La Trobe University. https://latrobe.libguides.com/maths/hypothesis-testing
More about hypothesis testing. (n.d.). https://bolt.mph.ufl.edu/6050-6052/unit-4/module-12/more-about-hypothesis-testing/
Hypothesis testing is using multiple statistical tests to determine whether or not an outcome happens by chance or if it is affected by a true effect. A researcher will typically begin their study with the development of a null hypothesis, which essentially assumes there is no difference between sample populations, and it will have no effect on the study. A confidence interval is a range of values that described uncertainty around an estimate (US Census Bureau, 2021). An example of this in practice is that if a researcher has a confidence interval of 95%, it means that there is a 95% chance that the effect of the study will fall within the values provided by the interval.
Both of these are statistical tools that are used in healthcare research to allow researchers to draw conclusions about the effectiveness of treatments or other related interventions. When conducting these studies, it is important to assess the magnitude of the effect when using confidence intervals. A wide confidence interval means that the estimate will be less precise and there is more uncertainty, whereas if the confidence interval is narrow, it means that the clinical significance is greater.
An example of these is if a researcher is evaluating the effectiveness of a new drug. They will start by developing a null hypothesis which will have no effect on the outcome of the study but will provide a foundation for the direction of the study. If the researcher predicts a narrow confidence interval and it is accurate, this will provide more clinical significance to their study. Hypothesis testing and confidence intervals both use the same underlying methodology to prove or disprove an outcome in statistical testing (Frost, 2022).
Bureau, U. S. C. (2021, October 8). A basic explanation of confidence intervals. Census.gov. https://www.census.gov/programs-surveys/saipe/guidance/confidence-intervals.html#:~:text=A%20confidence%20interval%20is%20a,the%20uncertainty%20surrounding%20an%20estimate.
Frost, J. (2022, March 17). Hypothesis testing and confidence intervals. Statistics By Jim. https://statisticsbyjim.com/hypothesis-testing/hypothesis-tests-confidence-intervals-levels/
Job there are a big significance confidence interval provides a range of values within given confidence (e.g., 95%), including the accurate value of the statistical constraint within a targeted population. Most research uses a 95% CI, but investigators can set any level (e.g., 90% CI, 99% CI). A CI provides a range with the lower bound and upper bound limits of a difference or association that would be plausible for a population.Therefore, a CI of 95% indicates that if a study were to be carried out 100 times, the range would contain the true value in 95,confidence intervals provide more evidence regarding the precision of an estimate compared to p-values.
In consideration of the similar research example provided above, one could make the following statement with 95% CI:
Statement: Individuals who were prescribed Drug 23 had no symptoms after three days, which was significantly faster than those prescribed Drug 22; there was a mean difference between the two groups of days to the recovery of 4.2 days (95% CI: 1.9 – 7.8).
It is important to note that the width of the CI is affected by the standard error and the sample size; reducing a study sample number will result in less precision of the CI (increase the width).A larger width indicates a smaller sample size or a larger variability.A researcher would want to increase the precision of the CI. For example, a 95% CI of 1.43 – 1.47 is much more precise than the one provided in the example above. In research and clinical practice, CIs provide valuable information on whether the interval includes or excludes any clinically significant values.
Hypothesis testing and confidence intervals are significant contributors to evidence-based practice and guide the protocols of patient intervention, ultimately influencing patient outcomes. Hypothesis testing can be employed to correlate associations between variables, which leads to quality improvement and supports improvements to standard practice. However, it is the role of nursing leadership to responsibly interpret research and apply it for the improved patient outcomes (Ambrose, 2021). Confidence intervals represent the risk that the research could be wrong. The combined use of hypothesis testing with confidence intervals allows for results to be extrapolated and applied to the general population. While the confidence interval can be important in applying the research results from the sample to the population, it is important to be aware of the ability to manipulate the confidence interval if thorough methods are not employed. Small sample size and lack of replication are both contributing factors to misinterpreted p-values or statistical significance.
In the example of a 2019 study by Henson et al., the authors compared select outcomes associated with postoperative analgesic approaches in patients undergoing TKA. The two approaches were femoral nerve blocks and periarticular injections. Using 2-tailed t tests, the researchers examined pain perception, use of opioid analgesics, length of stay and total cost of care. Additionally, they examined readmission rates using a 2-sample z test for proportions. With a stratified population sample of 144 patients, the authors found an association between patients who received an FNB with a lower pain perception (p = .0497). Results also showed a possible correlation between a decrease in opioid consumption in those who received a PAI (p = .037). By calculating the mean, along with the standard deviation of the five selected variables, the authors were able to gain data regarding reported decrease in pain, it is clear that the study needs further replication with additional factors examined (Henson, et al., 2019). However, studies like this set the groundwork for future studies to build upon and gain even more in-depth knowledge regarding the topic or practice at hand.
Ambrose, J. (2021). Clinical inquiry and hypothesis testing. In Grand Canyon University (Ed.). Applied statistics for health care (ch.3). https://bibliu.com/app/#/view/books/1000000000581/epub/Chapter3.html#page_31
Henson, K. S., Thomley, J. E., Lowrie, L. J., & Walker, D. (2019). Comparison of Selected Outcomes Associated with Two Postoperative Analgesic Approaches in Patients Undergoing Total Knee Arthroplasty. AANA Journal, 87(1), 51–57.
Confidence intervals (CIs) are a range of values that are likely to include an unknown parameter in a population. Since confidence intervals tend to contain the parameter, they serve as reliable estimates of the population parameter. A confidence interval consists of a point estimate (the most likely value) and a margin of error around that estimate. Margins of error indicate how much uncertainty surrounds the sample estimate of a population parameter. CIs reflect the risk of the researcher being incorrect. Analyzing the data statistically and determining its associated probability is extremely significant. CIs of 95% are used to determine whether to reject or not to reject the null hypothesis. The confidence interval of 95% indicates that 95% of research projects like the one completed will include the true mean. However, 5% will not, indicating there is a five percent chance of being wrong. Reducing the confidence interval increases the risk of error. (Ambrose,2018). In exploratory studies, p-values enable the recognition of statistically significant findings. Confidence intervals provide information about a range in which the true value lies with a certain degree of probability. They also provide information about the direction and strength of the demonstrated effect. The size of the confidence interval depends on the sample size and the standard deviation of the study groups. If the sample size is large, this leads to “more confidence” and a narrower confidence interval (Du Prel, Hommel, Rodrig & Blettner,2009).
An example of use of both hypothesis testing and confidence interval can be seen in a study of “the difference between the mean decrease in blood pressure with a new and with an old antihypertensive. Nurses give antihypertensive to patients in their daily practice. Treatment of hypertension in elderly people can reduce cardiovascular morbidity and mortality, which is a very important benefit. On given example, null hypothesis of the study will say there might be no difference between two antihypertensives with respect to their ability to reduce blood pressure. The alternative hypothesis then states that there is a difference between the two treatments. If P value is less than 0.05 (or 5%), the result is significant, and it is agreed that the null hypothesis should be rejected and the alternative hypothesis—that there is a difference—is accepted. However, use of confidence interval helps to conclude the statistical significance in this study. The difference of the mean systolic blood pressure between the two treatment groups, the question is whether the value 0 mm Hg is within the 95% confidence interval (= not significant) or outside it (=significant) (Du Prel et al,2009). In above example if the result of P value is less than the significance level indicates that results are statistically significant.
Du Prel, J. B., Hommel, G., Röhrig, B., & Blettner, M. (2009). Confidence interval or p-value? part 4 of a series on evaluation of scientific publications.Deutsches Arzteblatt international, 106 (19), 335-339. https://doi.org/10.3238/arztebl.2009.0335
Ambrose, J. (2018). Clinical Inquiry and hypothesis testing. In Applied statistics for health care(1 ed.).Chapter 3. Grand Canyon University. Retrieved from https://lc.gcumedia.com/hlt362v/applied-statistics-for-health-care/v1.1/#/chapter/3
Hansson L, Lindholm LH, Ekbom T, Dahlof B, Lanke J, Schersten B, Wester P, Hedner T, de Faire U, Hansson, L., Lindholm, L. H., Ekbom, T., Dahlöf, B., Lanke, J., Scherstén, B., Wester, P. O., Hedner, T., & de Faire, U. (1999). Randomized trial of old and new antihypertensive drugs in elderly patients: cardiovascular mortality and morbidity the Swedish Trial in Old Patients with Hypertension-2 tudy. Lancet , 354 North American Edition(9192), 1751-1756. https://pubmed.ncbi.nlm.nih.gov/10577635/
The null hypothesis acts like a punching bag: It is assumed to be true in order to shadowbox it into false with a statistical test. When the data are analyzed, such tests determine the P value, the probability of obtaining the study results by chance if the null hypothesis is true. The null hypothesis is rejected in favor of the alternative hypothesis if the P value is less than alpha, the predetermined level of statistical significance (Daniel, 2000). “Nonsignificant” results — those with P value greater than alpha — do not imply that there is no association in the population; they only mean that the association observed in the sample is small compared with what could have occurred by chance alone. For example, an investigator might find that men with family history of mental illness were twice as likely to develop schizophrenia as those with no family history, but with a P value of 0.09. This means that even if family history and schizophrenia were not associated in the population, there was a 9% chance of finding such an association due to random error in the sample. If the investigator had set the significance level at 0.05, he would have to conclude that the association in the sample was “not statistically significant.” It might be tempting for the investigator to change his mind about the level of statistical significance ex post facto and report the results “showed statistical significance at P < 10”. A better choice would be to report that the “results, although suggestive of an association, did not achieve statistical significance (P = .09)”. This solution acknowledges that statistical significance is not an “all or none” situation
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Hypothesis testing and confidence intervals are commonly used together in health care research to determine the likelihood that a certain result or outcome occurred due to a certain risk factor (Lambert & Niederhauser, 2018). For example, in a study to determine the effect of a certain drug on reducing the risk of complications after surgery, the researchers may use a hypothesis test to determine if the drug had an effect on the patients’ outcomes. If the results of the test indicate that the drug had an effect, the researchers may then use a confidence interval to estimate the range of effects that the drug had on the patients’ outcomes (Rigatto & Devereaux, 2020).
A workplace example of how hypothesis testing and confidence intervals can be used together in health care research can be seen in a study of the relationship between shift work and employee health. The researchers may use a hypothesis test to determine if there is a statistically significant correlation between shift work and employee health. If the results of the test indicate that there is a correlation, the researchers may then use a confidence interval to estimate the range of effects that shift work has on employee health (Grundy et al., 2020). This information can then be used to make informed decisions about the health of employees in shift work environments.
In conclusion, hypothesis testing and confidence intervals are commonly used together in health care research to identify and quantify the effects of risk factors on health outcomes. The results of these tests can provide valuable information for decision-making in the health care industry.
Grundy, A., et al. (2020). A systematic review of the relationship between shift work and employee health and well-being. International Journal of Environmental Research and Public Health, 17(11), 4033.
Lambert, B. S., & Niederhauser, V. S. (2018). Evaluating research in health care and the social sciences. Jones & Bartlett Learning.
Rigatto, L., & Devereaux, P. J. (2020). Estimating the magnitude of treatment effects: Confidence intervals and hypothesis tests. Canadian journal of anaesthesia, 67(2), 160-166.