Section 7.7Introduction 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?

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Example 16

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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.

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Homework

- Worksheet 7.7