Not so Difficult? This Parrot ‘Gets’ Statistical Inference
If you have tramped or climbed in New Zealand’s high country, as I did for a couple of months many moons ago, you’ve probably spent hours watching kea exploring or ‘playing’. Kea are large parrots with wicked-looking beaks that are highly social, and notorious among mountaineers for their ability to find food, no matter how hard you try to hide it.
This article in nature communications now presents evidence that kea can make probability judgments. Moreover, they can combine difference types of evidence in forming a probability judgment. In other words, they seem to understand statistical inference.
Here’s the abstract:
(Photo from here.) In the experiments, kea first learned that black, but not orange, sticks would bring a food reward. Then they were offered choices between an unseen stick taken from, for example, the left pot in the photo, and one taken from the right pot. Seeing the pot gave information about the relative probabilities of drawing black and orange from each pot.
I was alerted to this research by this article in The Conversation, which gives a nice summary, and includes some great photos, which are not for reproduction.
Yes, only 6 animals were tested, and I can’t see any sign that the experiments and analyses were preregistered. But full data, and method and analysis scripts are available online, and several experiments providing converging evidence are reported. Maybe replications are planned.
The authors used Bayesian analyses, most basically to assess the numbers of correct trials in blocks of 20 trials, where 10/20 would be expected by chance. For example, in Table 1 (here and scroll down) the proportion correct is given for each of 6 animals in 6 different conditions. Proportions with a Bayes Factor (BF) of 3.0 or more in favour of the ‘better than chance’ hypothesis are shown in bold; 27 of the 36 are bold. This illustrates a Bayesian dichotomous decision making strategy analogous to marking individual results with ‘*’ if their individual p value is less than .05. As usual, I’d hope for estimation results, whether using Bayesian credible intervals or frequentist confidence intervals–rather than either Bayesian or NHST dichotomous classification of results.
So, are kea smarter than some of our students, who seem to have such difficulty grasping the basics of statistical inference? Or is the problem that, at least in the past, we’ve insisted on burying inference under a weird superstructure of NHST and p values?
Who’s up for some experiments to check out how kea can go with confidence intervals?
P.S. It really is worth going high in New Zealand, and not only for the kea watching.