MATH 225N Week 6 Discussion 100% PASS

Confidence Intervals In everyday terms, a confidence interval is the range of values around a sample statistic (such as mean or proportion) within which clinicians can expect to get the same results if they repeat the ...

Confidence Intervals In everyday terms, a confidence interval is the range of values around a sample statistic (such as mean or proportion) within which clinicians can expect to get the same results if they repeat the study protocol or intervention, including measuring the same outcomes the same ways. As you ask yourself, "Will I get the same results if I use this research?", you must address the precision of study findings, which is determined by the Confidence Interval. If the CI around the sample statistic is narrow, you can be confident you will get close to the same results if you implement the same research in your practice. Consider the following example. Suppose that you did a systematic review of studies on the effect of tai chi exercise on sleep quality, and you found that tai chi affected sleep quality in older people. If, according to your study, you found the lower boundary of the CI to be .49, the study statistic to be 0.87, and the upper boundary to be 1.25, this would mean that each end limit is 0.38 from the sample statistic, which is a relatively narrow CI. (UB + LB)/2 = Statistic [(1.25 + .49)/2 = .87] Keep in mind that a mean difference of 0 indicates there is no difference; this CI does not contain 0. Therefore, the sample statistic is statistically significant and unlikely to occur by chance. Because this was a systematic review, and tai chi exercise has been established from the studies you assessed as helping people sleep, based on the sample statistics and the CI, clinicians could now use your study and confidently include tai chi exercises among possible recommendations for patients who have difficulty sleeping. Now you can apply your knowledge of CIs to create your own studies and make wise decisions about whether to base your patient care on a particular research finding. Initial Post Instructions Thinking of the many variables tracked by hospitals and doctors' offices, confidence intervals could be created for population parameters (such as means or proportions) that were calculated from many of them. Choose a topic of study that is tracked (or that you would like to see tracked) from your place of work. Discuss the variable and parameter (mean or proportion) you chose, and explain why you would use these to create an interval that captures the true value of the parameter of patients with 95% confidence. Consider the following: How would changing the confidence interval to 90% or 99% affect the study? Which of these values (90%, 95%, or 99%) would best suit the confidence level according to the type of study chosen? How might the study findings be presented to those in charge in an attempt to affect change at the workplace? Confidence intervals are an estimated range of values from a set of sample data that is likely to include the true mean of the population [ CITATION Hol17 \l 1033 ]. Confidence intervals provide us the upper and lower limits around the sample mean, and within the interval, we can be confident that we captured the population mean. Most often you will see confidence intervals of 95% or 99%. The 95% confidence interval is most frequently used because as the confidence level increases the margin of error also increases. This also widens the interval and sometimes it can become so large that the data is useless. An example of this would be I am 99% certain that you will score between 10 and 100 on your next exam. Blood glucose control is something constantly measured within healthcare especially in critically ill patients. There are various blood glucose control algorithms, confidence intervals could be used to determine the algorithm that delivers the best glucose control within similar sample patient groups. If in a sample group of 30 patients the mean glucose was 111 the confidence interval for a 95% confidence level would be 102-120. The interpretation of this could mean that 95% of the patients using this glucose control algorithm can expect to have blood glucose levels between 102-120. Analyzing data in this manner could help the facility select the most efficient glucose control algorithm for the patients.