Section 7.7 Introduction to Inference Special Topics

Statistical Inference Statistical inference refers to methods for drawing conclusions about an entire population on the basis of data from a sample. Since populations are usually too large for us to get 100% accurate information (that is, to survey everyone!), we use results from sampling to get an estimate of what true population values are. Statistical inference works only if the data come from a random sample or randomized comparative experiment.

Confidence Intervals A confidence interval is one type of inference method. A confidence interval is a range of values, calculated from sample data, where we believe a true population value might actually be, with a certain level of confidence. True population values are called parameters.

Parameters vs. Statistics A parameter is a fixed (usually unknown) number that describes a population. A statistic is a number that describes a sample. The value of a statistic is known when we have taken a sample, but it can change from sample to sample. We often use a statistic to estimate an unknown parameter. Example 15 - Do You Find Shopping Frustrating?

Example 16

Sampling Distribution The sampling distribution of a statistic is the distribution of values taken on by the statistic in all possible samples of the same size from the same population. We want to describe sampling distributions for center, shape and spread.

Homework Worksheet 7.7