Workshop Statistics pp 279-293 | Cite as

# Confidence Intervals I

## Overview

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.

## Keywords

Population Parameter Simple Random Sample Dence Interval Population Proportion Sample Proportion## Preview

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