DQ: How would changing the confidence interval to 90% or 99% affect the study?
Confidence interval refers to the interval estimate around the mean or average. In other words, confidence interval may refer to the set of values around or close to the mean either in the negative or positive ways. In most cases, statistical studies do not have 100% confidence or certainty that the outcome of data analysis will be true. In the research process, there is always the likelihood that a hypothesis can be accepted (failed to be rejected) or rejected; this is always attributed to the confidence interval (Ambrose, 2018). Confidence intervals are important in data analysis because they aid in the determination of the accuracy of the mean. A confidence with smaller range indicates that the estimates are more accurate. On the other hand, when there is huge range or larger figure, the estimate may be considered inaccurate. To better understand the concept of confidence interval, the illustration below is a basic and more accurate definition. A confidence interval of 95% indicates that 95% of the studies will incorporate the true mean; on the other hand, 5% of the studies will not. In other words, there are five out of 100 that the research is wrong.
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Confidence interval may also refer to the range of values from given sets of sample data that are most likely to include the true mean of the population. Confidence intervals often form or are used to determine the accuracy in the data analysis processes. With the confidence interval, an individual can be sure that they have captured the mean of a given population. When the confidence level is so small, there is a high possibility of obtaining accurate outcomes in the data analysis processes. On the other hand, there might be a problem in the long run. For instance, if one says that they are certain of scoring 99%, the range of the data being calculated may be so big. For example, an individual may be 99% certain of scoring 10 to 100 on the examination ( Peterson & Kegler, 2020).
In the healthcare undertakings, there are different measurements that are always recorded. These measurements are often recorded with much accuracy using the mean and confidence level. For example, blood is something that is often measured in the healthcare system for the critically ill patients. There are various approaches of controlling blood glucose levels. Confidence interval can be applies to formulate correct approaches of delivering the best glucose control mechanisms. In most cases, hypothesis testing and confidence interval are applied together in the healthcare processes to determine the correlation that exists. Confidence intervals are essential approaches in statistical analysis.
Confidence Interval uses data from a sample to estimate a population parameter and hypothesis testing using data from a sample to test a specified hypothesis. Both hypothesis testing and analysis of confidence interval can aid in answering the research questions, the objectives of the research and the hypothesis formulated based on the research questions. Hypothesis testing and CI are used together in health care research to determine the correlation of variables to establish a probability value for improving patient outcomes in certain populations in the clinical setting.
Ambrose, J. (2018). Clinical inquiry and hypothesis testing. Grand Canyon University. Retrieved from https://lc.gcumedia.com/hlt362v/applied-statistics-for-health-care/v1.1/#/chapter/3
Peterson, A. B., & Kegler, S. R. (2020). Deaths from Fall-Related Traumatic Brain Injury – United States, 2008-2017. MMWR: Morbidity & Mortality Weekly Report, 69(9), 225–230. https://doi-org.lopes.idm.oclc.org/10.15585/mmwr.mm6909a2