Section 7.7 Introduction to Inference

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Section 7.7Introduction to Inference

• Special Topics

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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.

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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.

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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