Friday, January 8, 2021
Monday, December 21, 2020
You know what bugs me? How much time different intro stats textbooks spend talking about probability, lots of A not B stuff*, lots of probability associated with the normal distribution, etc. But we don't take advantage of the discussion to warn their students about the evils of relative vs. absolute risk. #statsliteracy
Relative risk is the most clickbaity abuses of statistics that there is. Well, maybe the causal claims based on correlational data are more common. But I think the relative risk is used to straight-up scare people, possibly changing their behaviors and choices.
I thought of it most recently when The Daily Mail (bless) used explained the difference in COVID-19 risk between dog owners and non-dog owners.
Here is the data described in the headline, straight from the original paper:
Really, Daily Mail? How dare you.
I think the most clever, trickiest, sneakiest ways to mislead with data are by not lying with data at all. Most truncated y-axes display actual data. Data sets with sampling error are still data sets. And relative risk and absolute risk express the same data, but relative risk sounds scary while absolute risk doesn't sound scary.
Another example I like involved Gerd Gigerenzer, the well-known cognitive psychologist. In this video, he describes an instance when relative risk headlines scared women away from oral contraceptives, and then there was a rise in abortions. All of which could have been avoided totally if women have been given absolute risk information about birth control pills.
You can take issue with Gerd's claim that abortion negatively affects women, but I think that most people would agree that women being scared out of taking their birth control pills is a bad thing. And that newspapers need to be responsible, and editors must avoid using relative risk to scare readers.
I know we have a lot of ground to cover in Intro Stats. But we are doing our students a disservice if we aren't preparing them to deal with oft-encountered dirty data in real life. This topic doesn't take too long to explain. You already have to talk about probability. The video is short. It is easy to explain.
*And probability has its time and place. Go read The Drunkard's Walk. You'll love it. But I just sit and think a lot about how we should use our precious Intro to Stats time. And I think we should point out how classroom topics play out in real life.
Monday, December 14, 2020
Have you ever heard of the theory that there are multiple people worldwide thinking about the same novel thing at the same time? It is the multiple discovery hypothesis of invention. Like, multiple great minds around the world were working on calculus at the same time.
Well, I think a bunch of super-duper psychology professors were all thinking about scale memes and pedagogy at the same time. Clearly, this is just as impressive as calculus.
Who were some of these great minds?
1) Dr. Molly Metz maintains a curated list of hilarious "How you doing?" scales. 2) Dr. Esther Lindenström posted about using these scales as student check-ins. 3) I was working on a blog post about using such scales to teach the basics of variables.
So, I decided to create a post about three ways to use these scales in your stats classes:
1) Teaching the basics of variables.
2) Nominal vs. ordinal scales.
3) Daily check-in with your students.
1. Teach your students the basics of variables
Here is a slide I use in my own classes to introduce scales, response options, score, anchors.
I also use this scale in particular because it is an example of the fact that all scales need a "Not Applicable" option. Vegetarians, Jews, and Muslims don't want any of this bacon, thankyouverymuch. The lack of a n/a option is my psychometric pet peeve.
If you are a stats professor who uses a Begining of the Year Survey, you could add one of these scales to that survey and then use it in class the way I do.
When I use the silly bacon scale, I usually pair it with the more widely used Wong-Baker to show that images can be used as anchors in serious contexts for serious reasons. Such anchors are helpful for non-English speakers, injured patients who can't speak, and when you need to operationalize the abstract. Many of your students have seen the Wong-Baker scale, so you are taking your silly example and grounding it in reality.
2. Teach your students the difference between nominal vs. ordinal data:
Some of these scales are ordinal, like Dr. Karen Errichetti's scale of Fauci: