One of the major applications of statistics is estimating populationparameters from
sample statistics.
For example, a poll may seek to estimate the proportion of adult residents
of a city that support a proposition to build a new sports stadium. Out
of a random sample of 200 people, 106 say they support the proposition.
Thus in the sample, 0.53 of the people supported the proposition. This
value of 0.53 is called a point
estimate of the population proportion. It is called a point estimate
because the estimate consists of a single value or point.

The concept of degrees of freedom and its relationship to
estimation is discussed in Section B. Section C, "Characteristics
of Estimators," discusses two important concepts: bias and precision.

Point estimates are usually supplemented by interval
estimates called confidence
intervals. Confidence intervals are intervals constructed using
a method that contains the population parameter a specified
proportion of the time. For example, if the pollster used a
method that contains the parameter 95% of the time it is used,
he or she would arrive at the following 95% confidence interval:
0.46 < π < 0.60.
The pollster would then conclude that somewhere between 0.46
and 0.60 of the population supports the proposal. The media
usually reports this type of result by saying that 53% favor
the proposition with a margin of error of 7%. The sections
on confidence intervals show how to compute confidence intervals
for a variety of parameters.