Confidence Interval (CI) is an estimated interval that is calculated from a population parameter, and it is determined by the observations made on a given population (Knezevic 73). Essentially, by using the CI, an individual can provide values that fall in the range where the true value falls. From this statement, we can come up with a statement that the wider the confidence interval is, the more the variability in the sample, and the wider the CI, the less the precision of the point estimate.
The Confidence Interval is vital in statistical hypothesis testing because when an individual wants to test the null hypothesis against a given alternative, then the performance of the test is dependent on the confidence interval (Knezevic 73).
Statistics entails the study of population parameters, and this is where measures of central tendency like mean come in. The calculation of standard deviation and variance also help in the study of population parameters, and this is a concept in statistics. The bounding of mean and standard deviation greatly depends on the Confidence Intervals.
In statistics, individuals can decide to come up with conclusions based on probabilities of the likelihood theory. In the process, construction of estimates is important, and it depends on the construction of confidence intervals (Gilliland and Vince 5).
When selling a product to a population, an individual has to understand the average level of satisfaction among the consumers, or the percentage of the population that is likely to accept the product. The role of the confidence interval in such a situation is that it gives a hint about the most likely range of the percentage or average of the unknown population.
Gilliland, Dennis, and Vince Melfi. “A note on confidence interval estimation and margin of error.” Journal of Statistics Education 18.1 (2010): 1-8.
Knezevic, Andrea. “Overlapping confidence intervals and statistical significance.” Cornell University, StatNews 73 (2008).