The Number of Hypertension Cases recorded every day for one Month in a Clinic
The mean would be the most significant parameter to track the number of hypertensive patients reporting to a clinic every day in one month. It is a preferable measure for this study because it incorporates all the values in the calculation, unlike other parameters that only use a section of the data. It shows the number of hypertensive patients the clinic is likely to record in a single day irrespective of the hour, the conditions, and the situation. The study “the number of hypertensive patients reporting to a clinic every day in one month” has primarily been chosen because such cases are on a rising trend, and it’s critical to quantify them in the clinic.
It is, however important, to note that the mean is not always sufficiently comprehensive and often needs to be backed up by confidence interval to include any error that might have been committed in the study process (Bolstad and Curran, 2016). The confidence interval gives the lower and upper limits around the sample mean, within which the population mean is likely to fall. Using both the mean and the confidence intervals, one can easily enumerate the range of values the population mean could assume when evaluated against the study. For this study, 95% CI would be used to generate a range of values that we can be 95% sure that the population mean would be within.
In addition to 95% CI, we can as well use 90% CI and 99% CI. However, the range of values that would be generated from each interval would differ (Holmes, Illowsky, and Dean, 2017). For instance, 95% CI would provide a narrower range of values than 99% CI and a broader range than 90% CI. Ideally, the probability of finding the population mean within a narrow range as provided by 90% CI is lower as opposed to that provided by 99% CI. This relative variation in the range of values provided by each CI explains why 99% CI is the most accurate and probably preferred by many studies.
References
Bolstad, W. M., & Curran, J. M. (2016). Introduction to Bayesian statistics. John Wiley & Sons.
Holmes, A., Illowsky, B., & Dean, S. (2017). Introductory business statistics. OpenStax. https://openstax.org/details/books/introductory-business-statistics