This is an informal writing assignment– it does not need to be written perfectly but it does need to be readable!.
Please give examples of EIGHT instances of false or misleading information online, from your own online activities (not from class), from the types of false or misleading errors described in EIGHT DIFFERENT WEEKS OF CLASS. You need not cover every week, you need not offer more than eight examples, but please give EIGHT examples from DIFFERENT weeks. Please provide the error and some support for your own determination (better research). This is an informal writing assignment– it does not need to be written perfectly but it does need to be readable!
Each student must create a log of errors they find, representing one error from each day’s worth of errors. For instance, we will discuss direction problems and omitted variable bias on the same day—students’ logs must offer an example of either one of these errors (NOT BOTH) to satisfy the assignment for that day.
8 in total
Here is a non-exhaustive list of issues that have come up over the course of class, for you to consider if you haven’t been putting your error log together this whole time.
Read beyond the headline
Ask for sources
Check the quality of the paper—bias, honesty (chart made available)
Make sure it’s an actual news site
Check the author
Double check the actual sources (any official stats should be finadble)
Double check key words—arrests crime, crime vs. violent crime
Check Snopes, Politifact
Double check the date
For surveys check the questions and answers (“Do you like this class a) a lot, b) a ton, c) more than any other class” is not a reliable survey that will give reliable information)
Remember wikipedia is often edited by the people you’re looking up and by people with a stake in the argument.
Looking for general fishinessLook for unfair comparisons (apples and oranges—“urban areas” vs. “metropolitan areas”)
Changes in technology change what you should expect
Try to find alternate hypotheses that could explain it—maybe my makeup doesn’t give me migraines, maybe it’s the exhaustion
Contextualize all numbers (how much caffeine is in 99% caffeine free hot chocolate? How does it compare to things that you know keep you awake? How much fat is in whole milk? How does that compare to how much fat is in 2% milk?)
Direction issues
Ommitted variables
Spurousness
Data dredging/data fishing/p hacking
Right censoring (when time has cut off the correct comparison—remember how hip hop guys seem to die young as compared to country music stars?)
Means, medians, and modes
Can’t find a mean for a categorical variable
Even for continuous variables, outliers (super rich people) mess with the numbers
Overreliance on P values—the prosecutor’s fallacy (In New Jersey, matching DNA at the scene only means there’s a 1 in 10 chance that you’re the murderer).
New, not replicated studies
Studies that can’t be replicated
Studies where they won’t show you the data
Small sample size
Small effect size
Too many variables (p hacking)
Too new a design
Too few people finding those results in a really hot field
Retractions
Open access (predatory journals)
All the bad science
Inappropriate comparisons (of mice and men)
Misleading axes
Lack of a zero
Percentages that don’t add up to anything
Lack of description of chart and what is represented
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