Confidence Intervals

Confidence Interval
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Confidence Interval
Confidence interval (CI) is the range of values or data derived from a given statistics assumed to include the “true” or actual population value comprehensively (Lang et al., 2006). Researchers have been using CI to conclude a study population because of the presumption that CI closely represent the population. Two samples from a given population cannot generate similar CI; however, repetition of the samples many times will create a CI the unknown population parameter. Researchers calculated CI for either mean or proportion data and reported in the results section of a published research (Lang et al., 2006). Statistical knowledge of CI is fundamental in clinical or mechanical decision-making.
The commonly used level is a 95% CI, other include 90% and 99% (Lang et al., 2006). Suppose the burn time of a sample light bulb is measured, and that 95% CI of the mean of burn time range from 1234-1270 hours. Therefore, the confidence interval will indicate to the researcher that there is a 95% chance that the mean value of the entire population of the light bulbs will fall within this range. An increase in the sample size decreases the sampling error, and thus the confidence becomes narrower. According to Lang et al. (2016) when the CI range is smaller, then the sample value is likely to represent the population value. Notably, in a case where there is CIs of two mean scores, then one of the CI can conclude if the difference between the scores is statistically significant.

In summary, the statistical knowledge of confidence interval by researches is ideal in drawing conclusions about population values and making reasonable decisions. CI range width is a crucial indicator of how precisely the sample value represents the population value in a study. Moreover, understanding of the significance of narrower CI range will help a researcher approximate the equivalence of the sample value and the population value in question.
Reference
Lang, T.A., & Secic, M. (2006). How to Report Statistics in Medicine. (2nd ed.). Philadelphia: American College of Physicians