From the same guy who brought us spurious correlations, a fun way to show that correlation is not causation via graphs of correlations between very different things that do not cause each other.

I did attach an image but because of a Lemmy/Mbin issue I don’t think I can have actual alt text, so here is the alt text.

A website, whose title is “spurious scholar”, with the subtitle “Because if p < 0.05, why not publish?”

Step 1: Gather a bunch of data.

Step 2: Dredge that data to find random correlations between variables.

Step 3: Calculate the correlation coefficient, confidence interval, and p-value to see if the connection is statistically significant.

Step 4: If it is, have a large language model draft a research paper.

Step 5: Remind everyone that these papers are AI-generated and are not real. Seriously, just pick one and read the lit review section.

Step 6: …publish:

Then there are two screenshots from papers generated with this method.

Also, clicking the note for step 2 has some pretty educational content on being naughty with data, at least for me, someone who is not an academic:

“Dredging data” means taking one variable and correlating it against every other variable just to see what sticks. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.

Fun fact: the chart used on the wikipedia page to demonstrate data dredging is also from me. I’ve been being naughty with data since 2014.