Confidence limits and hypothesis testing are some of the key components of statistical testing that are used in healthcare administration for different purposes. Both of them are interconnected, as it is impossible to establish a hypothesis without knowing the confidence limits. The numbers indicate the lower and the upper ends of the confidence interval (Kros & Rosenthal, 2016). For instance, if a man is six and the confidence limits are 4 and 7, the confidence interval will be 4 to 7. They indicate how accurate an estimate of the mean will be for a particular statistical category of objects or characteristics. Confidence limits enable medical personnel or healthcare administrators to establish how close the estimate they have is likely to be to the one that exists. In order to count the confidence limits for a particular object, one needs to calculate a confidence interval.
Hypothesis testing helps establish whether a particular statement or hypothesis is true and determines how plausible it is. In order to find it out, the statists usually use sample data from a particular population, relying on the characteristics they need. The hypothesis is divided into two large groups. The first one is called the null hypothesis, and it is a statement that indicates that there is no statistical difference between two or more variables (Kros & Rosenthal, 2016). The second type is the alternative hypothesis, and which is opposed to the null hypothesis and is what statists usually check.
When speaking about hypothesis testing, it is necessary to mention the two types of errors healthcare assistants may face while trying to establish the validity and relevance of a particular statement. The error of the first type is also called a False Negative. It may appear when a statistical analysis concludes that the null hypothesis is false and irrelevant when, in fact, it is true (Kros & Rosenthal, 2016). The second type of error is called False Positive, and, unlike in the case of the errors of the first type, they conclude that the null hypothesis is true when in reality, it is false.
Hypothesis testing alongside the confidence intervals is the two most important constituents of the research methodology in all types of research, including medical ones. They serve as guidelines in planning, realizing, and establishing the final results during the process of conducting a study. If the healthcare providers do not understand these two methods and do not pay attention to the statistical and clinical differences, they may be unable to establish the right diagnosis for a patient (Williams & Wan, 2016). It is necessary to remember that all doctors may be considered God’s missionaries who have the skills and knowledge to treat them ( English Standard Version Bible , 2001, Luca 9:1). However, they will not be able to prescribe adequate treatment for the patients without the right diagnosis. Despite the significance of the methods mentioned above, it is necessary to rely on quantitative studies as well, as their usage may give a broader outlook on the disease (Pope et al., 2002). Hence, doctors should not consider confidence limits and hypothesis testing as the only medical research methods.
Taking into account all mentioned above, it is possible to conclude that hypothesis testing and confidence intervals are connected with each other. Both of them belong to the category of inferential techniques, and they use samples in order to either estimate a characteristic of a particular population or check the validity of a statement. Hence, confidence intervals and confidence limits, in particular, are used to estimate sampling parameters, while hypothesis testing is a method that shows whether the hypothesis is reliable or not. Both these methods can be used either in tandem or separately in order to help healthcare assistants support their conclusions or establish the relevance of particular suppositions.