Oops! A day late with this one because work, exercise, and the AI detector itself sucked the last minute out of me.

Better AI code comment detector

Following on from last week's article, this is that promised web version based on open data. It's better (77 % accuracy plus calibrated predictive percentages!), flashier (look at them colours!), and a lot of fun to use.

Full article (13–28 minute read): Better AI code comment detector

Flashcard of the week

Hosmer and Lemeshow have co-authored two books called Applied Survival Analysis and Applied Logistic Regression. I like both a lot. There's huge overlap between them because they follow the same general formula for teaching model-building etc. and they only diverge in the type of analysis they teach. But they're good at going through the basics of how to think about applying statistics in practice.

Here's a flashcard from the logistic regression book.

What must be true about the experimental (or observed) groups for there to be a confounder?

I think the answer here is particularly clear. Many people involved with statistics to some degree have an intuition for what makes a confounder, but few people can put it down into a small set of words that capture the concept.

Here's Hosmer and Lemeshow's attempt:

The groups must be drawn from slightly different populations, such that a trait (often associated with the outcome) is inadvertently present to a greater degree in some groups than others.

In a concrete example, maybe I have n=2,400 study subjects, and split them into two groups of 1,200 each. I send one group free loose leaf tea, and the other teabags, and I keep doing this for five years. Then I measure all-cause mortality and find out the group that received teabags dies sooner.

If the groups were completely randomised, it would seem like drinking tea bagged is dangerous. The randomisation allows us to measure the risk of getting this result by accident.

But if I reveal I made the group split based on whether or not each subject's first name included the letter "A" but not the letter "J", then the difference in mortality is due to a confounder! That selection rule produces roughly evenly-sized groups but one group will contain only 42 % males and the other a whopping 60 % males. The outcome (mortality) is associated with a difference in the populations that comprise the groups, rather than the treatment.

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