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Normal probability distributions, z-scores, and the central limit theorem help us to estimate population parameters. Confidence intervals are an important topic in statistical analysis. The topic offers literally thousands of applications in almost every profession today. 95% (confidence) has been found to be a convenient level for conducting scientific research, thus making it as a traditionally most used confidence level. For example, the estimate of my midterm score in this class is 93. I am more likely to be accurate (and precise) to estimate a 95% confidence interval within which my actual score is suppose to fall somewhere between 84 and 96. When using 99% confidence (interval becomes wider), suppose the score is somewhere between 30 and 96, the more accurate I am likely to be that my midterm score will probably fall within that large interval, but poor precision. In this case, the smaller (or narrow) confidence level, the higher estimate accuracy.