Statistical thinking: What data would you need to collect to disprove the predictive power of astrological signs?

Okay. I haven't used this in class yet because it is July, and I just found it. However, I will open the Fall 2024 semester with this example. It is fun and accessible and shows how research can be used to study whether or not personality varies based on astrological signs.

I will start by showing them a bunch of funny astrology memes (see above). Then, I'll ask them to think of ways to design a study to prove that astrology is/is not bunk. What sort of data would they need to collect to do this?

Then, I'm going to show them this study (Joshanloo, 2024):

https://onlinelibrary.wiley.com/doi/epdf/10.1111/kykl.12395?domain=author&token=BKSRDREAX9F3BKAWGVBD

Statsy things to share with your students:

1. Archival data: The used repurposed, vintage, federal data. The General Social Survey, to be specific. Data scientists are trained to see the potential of random data sets.

The horoscope sign was simple to determine since the GSS collects birthday data. The author was able to pick this personality data out of the GSS:

2. The author then did a shit load of ANOVAs to see if scores on those scales varied by astrological sign. They did not. For an example, see the data visualization for Life Is Dull. Nothing is going on—not a thing. Like...it is meta-dull.

3. Some of the GSS data items had two or three response options, and they used Kruskal-Wallis tests on those. I think this is the first example I've ever had of that test. There are also some ANCOVAs.

4. There are non-significant/small effect size findings for DAYS. Sometimes, these are hard to find in published research. It is useful to show our junior statisticians situations when the null confirms underlying research hypotheses.

Comments

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