Freakanomics Radio's "America's Math Curriculum Doesn't Add Up"

"I believe that we owe it to our children to prepare them for a world they will encounter, a world driven by data. Basic data fluency is a requirement, not just for most good jobs, but for navigating life more generally." -Steven Levitt

Preach it, Steve. This edition of the Freakonomics podcast featured guest host Steven Levitt. He dedicated his episode to providing evidence for an overhaul of America's K-12 math curriculum. He argues that our kids need more information on data fluency.

I'm not one to swoon over a podcast dedicated to math curriculums, but this one is about the history of how we teach math, the realities of the pressures our teachers face, and solutions. It is fascinating.

You need to sit and listen to the whole thing, but here are some highlights:

Our math curriculum was designed to help America fight the Space Race (yes, the one back in the 1960s). For a world without calculators. And not much has changed.

Quick idea for teaching regression/correlation

14:20: Economist Dr. Sally Sadoff had students perform regression by going out into the world and collecting two data points the students were interested in. She describes how one under-performer measured the relationship between hair spray use and hair damage, which was of interest to a make-up obsessed student.

Psychometrics lesson in the creation and revising of the SATs

23:30: HOLY SMOKES. Information on why the SATs were created and how they were totally biased in favor of the affluence. I think this would be an interesting case study in RM, good intentions, and the road to hell.

Another old SAT question includes this gem:

Arguments and information to share with your students as they wonder whether or not they will ever use stats:

Other interesting data about the current job environment:

7:40: 90% of the data ever created by humanity was created in the last two years. 7 of the 10 largest growing job titles are data related.

  36:40: They discuss a survey that asked participants what math they wish they had learned in high school. Participants wished they had more lessons in statistical literacy. Specifically, they wanted they knew how to analyze data to gain insights (65% of participants) and how to make data visualizations and use data to make an argument(60% of participants.).

Freakanomics also has a resource guide for math teachers: http://freakonomics.com/math/

Planet Money's The Modal American

While teaching measures of central tendency in Intro stats, I have shrugged and said: "Yeah, mean and average are the same thing, I don't know why there are two words. Statisticians say mean so we'll say mean in this class." I now have a better explanation than that non-explanation, as verbalized by this podcast: The average is thrown around colloquially and can refer to mode, while mean can always be defined with a formula.

This is a fun podcast that describes mode vs. mean, but it also describes the research the rabbit hole we sometimes go down when a seemingly straightforward question becomes downright intractable. Here, the question is: What is the modal American? The Planet Money Team, with the help of FiveThirtyEight's Ben Castlemen, eventually had to go non-parametric and divide people into broad categories and figure out which category had the biggest N. Here is the description of how they divided up :

And, like, they had SO MANY CELLS in their design. JUST SO MANY. For each response option, there were categorical responses:

How to use in class:

1. Podcasts - I know some of you use these in class, here is one for stats.
2. Categorical vs. continuous variables and when and why you would use them. Here, a continuous variable, income, was divided into categorical brackets because they were looking at the data in a non-parametric manner.

3. Which THEN allows you to discuss non-parametric tests, like this one. They weren't exactly doing a chi-square, right? But they were thinking like a chi-square. They just wanted to figure out which of their buckets contained the most Americans...however, they used non-parametric logic. And that logic for non-parametric, where you have groups, and not the general linear model, working in the background, IS challenging for a novice to understand.

Special thanks to Michael Proulx (@MicahelProulx) for recommending this episode.

Pedagogy article recommendation: "Introducing the new statistics in the classroom."

I usually blog about funny examples for the teaching of statistics, but this example is for teachers teaching statistics. Normile, Bloesch, Davoli, & Scheer's recent publication, "Introducing the new statistics in the classroom" (2019) is very aptly and appropriately titled. It is a rundown on p-values and effect sizes and confidence intervals. Such reviews exist elsewhere, but this one is just so short and precise. Here are a few of the highlights:

1) The article concisely explains what isn't great or what is frequently misunderstood about NHST.

2) Actual guidelines for how to explain it in Psychological Statistics/Introduction to Statistics, including ideas for doing so without completely redesigning your class.

3) It also highlights one of the big reasons that I am so pro-JASP: Easy to locate and use effect sizes.