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• ### Poem: Type Two

A poem about type II errors in diagnostic testing using a diabetes test context.  The poem was written by Lawrence Lesser from The University of Texas at El Paso and received an honorable mention in the non-song category of the 2023 A-mu-sing Competition.  The author also provided the following outline for a lesson plan:

Some sample questions (one per stanza) students can explore or discuss
as a practical application of statistics to a prevalent disease
that likely affects (or will) a friend or relative of almost everyone.

First stanza: Look up history of diabetes prevalence to explore questions such as: Is “1 in 10” roughly accurate for the United States and how does that compare to other countries? Was the 2003 lowering of the threshold for a prediabetes diagnosis based on updated medical understanding of the disease or more of a policy decision to give an “earlier warning”?

Second stanza: How does a hypothesis testing framework apply to an oral glucose tolerance test (OGTT)? It’s warned that a false positive is possible if the patient did not eat at least 150g of carbohydrates for each of the 3 days before the test. (This is likely what happened to the poet, whose diagnosis was overturned just 2 months later by an endocrinologist.)

Third stanza: Given the usual trend that the null hypothesis usually means no effect, no difference, nothing special, explain whether it seems consistent that a normality test such as Anderson-Darling would let normality be the null. When might it make sense for a doctor to view having a particular disease as the null hypothesis (and what would be the Type I and Type II errors?)?

Fourth stanza: Explain how having only a few individual values each day from a blood glucose meter (BGM) risks missing dangerously high variability of glucose (students can Google how high variability can be a risk factor for hypoglycemia and diabetes complications). Discuss how output from a Continuous Glucose Monitor (CGM) that records values every 5 minutes can be used to check, for example, that the coefficient of variation is sufficiently low (e.g., < 36%) and that “time in range” (e.g., 70-180 or 70-140 mg/dL) is sufficiently high. Example output is on page S86 of https://diabetesjournals.org/care/issue/45/Supplement_1.

Fifth stanza: Have students look up current FDA guidelines on how accurate over-the-counter BGM readings need to be (e.g., https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7753858/) and have them connect this to margin of error, confidence intervals, etc.

Sixth stanza: Find online the diabetes “plate method” of taking a circular plate (9” in diameter) for a meal where half of the plate would have non-starchy vegetables, a quarter having lean protein, and a quarter with carbohydrate foods such as whole grains. How do this breakdown and total quantity compare to a pie chart of a typical meal that you (or typical college undergraduates) eat?

• ### Joke: What do you do at work?

A joke for discussing the calculus prerequisite for an upper division probability course.  The joke was written by Dennis Pearl and Larry Lesser in October 2022.

• ### Song: Trimmed Mean

A song about the trimmed mean as a robust measure of distributional center.  The lyric was written by Dennis Pearl from Penn State University in May 2023 and may be sung to the tune of the KitKat jingle - music by Michael A. Levin and lyric by Ken Shuldman used in the KitKat candy bar advertisements since 1986.

• ### Cartoon: Chart Antics

A cartoon for discussing time series plots. Here an instructor might ask if the graph shown is a possible function of time.  The cartoon was created by American cartoonist Jon Carter in 2017.

• ### Cartoon: Clean Data

A cartoon to initiate a discussion about cleaning data.  The cartoon was created by American cartoonist Jon Carter.

• ### Cartoon: Data Silence

A cartoon to be used in discussing forecasting. The cartoon was created by American cartoonist Jon Carter.

• ### Cartoon: Low Expectations

A humorous cartoon that might be usefult to discuss the meaning of "Expectations" in the graph shown and how best to display the information provided in the two graphs if the relationship between expectations and sales is paramount. The cartoon was created by American cartoonist Jon Carter.

• ### Cartoon: Data Beggar

A cartoon to help discuss both the value and limits of making predictions with large amounts of data. The cartoon was drawn by American cartoonist Jon Carter in 2015.

• ### Cartoon: Data Dice

A cartoon to facilitate discussion of designing a useful data dashboard. The cartoon was drawn by American cartoonist Jon Carter in 2014.

• ### Cartoon: Data Fog Patch

A cartoon to aid in discussion of fog computin, which involves connections of citizen devices to connect to a cloud computing structure.  The cartoon was drawn by American cartoonist Jon Carter in 2013.