MAT 240 Week 1 Discussion | Southern New Hampshire University
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- 05 Jul 2022
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MAT 240 Week 1 Discussion | Southern New Hampshire University
1-1
Discussion: Population, Samples, and Bias
In
real-life applications, statistics helps us analyze data to extract information
about a population. In this module discussion, you will take on the role of
Susan, a high school principal. She is planning on having a large movie night
for the high school. She has received a lot of feedback on which movie to show
and sees differences in movie preferences by gender and also by grade level.
She knows if the wrong movie is shown, it
could reduce event turnout by 50%. She would like to maximize the number of
students who attend and would like to select a PG-rated movie based on the
overall student population's movie preferences. Each student is assigned a
classroom with other students in their grade. She has a spreadsheet that lists
the names of each student, their classroom, and their grade. Susan knows a
simple random sample would provide a good representation of the population of
students at their high school, but wonders if a different method would be
better.
You can review the student demographics
here: Module
One Discussion Data.
In your initial discussion post,
specifically address the following:
·
Introduce
yourself and describe a time when you used data in a personal or professional
decision. This could be anything from analyzing sales data on the job to making
an informed purchasing decision about a home or car.
·
Describe
an example of a sample of this student population that would
not represent
the population well.
·
Describe
another example of a sample that would represent
the population well.
·
Finally,
describe the relationship of a sample to a population and classify your two
samples as random, cluster, stratified, or convenience.
In your response posts to at least two
peers, discuss the following:
·
Choose two
different sampling
methods from among your peers' responses. Then, identify bias in each peer's
sample and explain how you think they could remove the bias from their sample.
USA 
India