Parameters are characteristics of a population. For example, the averageage of teachers in the United States is a parameter. It is oftenimpractical, if not impossible, to determine the value of the parameter. It ismuch easier to take a sample of thepopulation and compute a statistic. The average age of a randomly-drawn groupof teachers from various U.S. towns is a statistic. Two concepts areimportant when comparing statistics to parameters: degreesof freedom and confidence intervals.

The sample statistic is an estimate of the population parameter.Statisticians need a way to express how well their statistic estimates the population parameter. Confidence intervals provide a way for thestatistician to state the most likely boundaries for a given parameter.

The confidence interval has two parts:

1. the interval that the parameter is likely to fall within
2. how confident the statistician is that the parameter will fallwithin those boundaries

For example, say we draw a sample of teachers as in the above example. Fromour data, we may be 95% confident that the average age of U.S. teachers isbetween 35 and 43 years old. In other words, the 95% confidence interval is(35, 43).