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Reply 1<\/p>\n

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Hypothesis testing involves using statistics by analysts or researchers to determine the strength or weakness of an assumption on a resident’s parameter (Shreffler & Huecker, 2020). On the other hand, a confidence interval is a measure of statistics that offer a range of values, upper and lower limits, that can be plausible to a particular population. Confidence intervals use CI to show the upper and lower limit; hence a 90% CI would indicate that if that research undertaking can be done for up to 100 times, the valid values will be 90 (Shreffler & Huecker, 2020). Also, a confidence interval will only show the aptitude of analysis to come up with accurate results, and this can only happen if many intervals are assessed. Notably, the CI width is affected by factors like standard errors and the number of selected participants. Therefore, having a significant CI would indicate that a small sample size was taken. With these two measures of statistics, more evidence on a particular hypothesis is presented. One can develop a research hypothesis, and the confidence interval determines the inclusion or exclusion of significant values in medicine (Shreffler & Huecker, 2020).<\/p>\n

In a hospital, doctors decided to test the effectiveness of two medicines, drug A and drug B. The hypothesis was that drug A was much more effective than drug B. The healthcare professionals then undertook to assess this hypothesis’s truthfulness by researching on patient’s response to these two medications. In 4.2 days, those who took drug A indicated much faster healing than those who took drug B, with p= 0.009. hence, (95% CI: 1.9-7.8). This means that drug A was more effective than drug B; thus, the hypothesis stands, and also, with the big CI range, the results can be extrapolated to a larger population.<\/p>\n

References<\/p>\n

Shreffler, J., & Huecker, M. R. (2020). Hypothesis Testing, P Values, Confidence Intervals, and Significance. In StatPearls [Internet]<\/em>. StatPearls Publishing.<\/p>\n

Reply 2<\/p>\n<\/p>\n

As defined by Ambrose (2018):<\/p>\n