MATH 312 Lecture Notes - Lecture 8: Statistic, Statistical Parameter, Situation Two
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MATH312—Jayme Rosenquist
Chapter 29 — Introduction to Statistics and Sampling 10/04/18
29.1 – What are statistics?
• Statistic—a numerical value of a quantity used to describe data. Sometimes we use the
word statistics to describe the study and processes of collecting, organizing, and
analyzing data.
1) A principal is interested in knowing whether or not a school’s discipline standards are
fair. He asks the in-school suspended students.
The principal gets a limited pool of people who are also likely biased.
2) An animal shelter wants to know how the public feels about animal adoption fees. They
send out a survey to people on the mailing list that have adopted previously.
This is a limited demographic. You lose the opinion of the people you more want
to hear from, those who have not adopted, due to high adoption fees or otherwise.
3) The Democratic Party wants to know how he Americans feel about illegal immigration.
They send out surveys to everyone on their mailing list.
The responses they would get back would be heavily biased as they would only be
hearing from other democrats.
• Population—the entire group that is of interest. The group may be made up of items
besides people, such as tree, insects, chemical yields, etc.
• Sample—part of the population used to collect data.
• Ensuring that your sample is a proper representation (unbiased) of your population is one
of the most important aspects of statistics.
Situation 1 Situation 2 Situation 3
Population: student body Population: the public Population: Americans
Sample: Suspended students Sample: Previous adopters Sample: Democrats
• If the teacher asked everyone in the classroom about how they feel about public
transportation that would be a population parameter. If only asking 5 students, then that is
a sample statistic.
• Random Sample—in which every member of the population has an equal chance of being
selected for the sample. I.e. is it an unbiased random sample.
How to take bad samples:
• Convenience sample—sampling out of convenience. E.g. pulling a classroom to represent
the school population
o Not representative of population
o Blind bias