Sampling Techniques

• Statistics is the art and science of gathering, analyzing and making inferences from numerical information obtained in an experiment.
• The information is called data.
• Descriptive Statistics is concerned with collecting, organizing, and analyzing the data.
• Inferential Statistics is concerned with making predictions about the data.
• Assume we have a box full of different marbles,
  • We would like to determine the composure of the marbles by pulling some out and looking at them.
  • In the case, the collection of marbles is called the population
  • The ones we pull out are called the sample
• We sample from a population because it is
  • too expensive to look at everything. (Think ask everyone in the university a question.)
  • Impossible to look at everything. (Think open every can of dog food to make sure that it is of acceptable quality)
• We would like an unbiased sample or one that replicates the entire population.
• There are several sampling techniques.
  • A Random Sample selects items from the population completely at random.
    • Contact names drawn at random from the population of students.
  • A Systematic Sample can be used to look at every n^th item in the population.
    • Go through the list of students in the university and call every 100^th
  • A Cluster Sample consists of sampling at random from a geographic area.
    • Sample students from each town.
  • A Stratified Sample consists of sampling at random from a portion of the group.
    • Divide the population into class and sample randomly frome each class.
  • A Convenience Sample consists of an easy to take sample.
    • Ask the people in the student center their opinion.

The Misuse of Statistics

• poor samples.
• Misuse of words (average has several meanings), as does largest
• Incorrectly conducted experiments
• Bad graphs.
• Look at the pictures on page 840

Homework

• Page 838 15 to 24 odd.
• Page 841 3 to 20 odd.