Confidence Intervals I
In the last unit you explored how sample statistics (in particular, sample proportions) vary from sample to sample. The Central Limit Theorem has allowed you to make probability statements about a sample proportion falling in a certain interval, provided that one knows the value of the population proportion. The much more common problem is to estimate or to make a decision about an unknown population parameter based on an observed sample statistic. These are goals of statistical inference.
There are two major techniques of classical statistical inference: confidence intervals and tests of significance. Confidence intervals seek to estimate a population parameter with an interval of values calculated from an observed sample statistic. Tests of significance assess the extent to which sample data support a particular hypothesis concerning a population parameter. This topic extends your study of the concept of statistical confidence by introducing you to the construction of confidence intervals for estimating a population proportion.
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