One way of teaching about margin of error/confidence intervals is via political polling data.
Here is a good site that has a break down of polling data taken in September 2012 for the 2012 US presidential election. I like this example because it draws on data from several well-reputed polling sites, includes their point estimates of the mean and their margin of errors.
This allows for several good examples: a) the point estimates for the various polling organization all differ slightly (illustrating sampling error), b) the margin of errors are provided, and c) it can be used to demonstrate how CIs can overlap, hence, muddying our ability to predict outcomes from point estimates of the mean.
I tend to follow the previous example with this gorgeous polling data from Mullenberg College:
This is how sampling is done, son! While stats teachers frequently discuss error reduction via big n, Mullenberg takes it a step further by only polling registered voters who plan on voting in the upcoming election.
I have my students review the .pdf which contains 9/2012 pre-election voting data for various state and national elections. NOTE: I live in PA, making this data more applicable to my own students. You may want to look up data from your home state.
For extra credit, I have my students answer the following questions (which forces them to read for statistics):
a) How they define a "likely voter"?
b) What is the CI used?
c) What is the margin of error?
d) What is the n-size for this sample?
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| From mvbarer.blogspot.com |
Here is a good site that has a break down of polling data taken in September 2012 for the 2012 US presidential election. I like this example because it draws on data from several well-reputed polling sites, includes their point estimates of the mean and their margin of errors.
This allows for several good examples: a) the point estimates for the various polling organization all differ slightly (illustrating sampling error), b) the margin of errors are provided, and c) it can be used to demonstrate how CIs can overlap, hence, muddying our ability to predict outcomes from point estimates of the mean.
I tend to follow the previous example with this gorgeous polling data from Mullenberg College:
This is how sampling is done, son! While stats teachers frequently discuss error reduction via big n, Mullenberg takes it a step further by only polling registered voters who plan on voting in the upcoming election.
I have my students review the .pdf which contains 9/2012 pre-election voting data for various state and national elections. NOTE: I live in PA, making this data more applicable to my own students. You may want to look up data from your home state.
For extra credit, I have my students answer the following questions (which forces them to read for statistics):
a) How they define a "likely voter"?
b) What is the CI used?
c) What is the margin of error?
d) What is the n-size for this sample?
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