Comments on AngryMath: Concrete P-Value Demonstration

Raymond -- Thanks for the comment, really good stu...

2012-01-02T11:37:12.278-05:00

Raymond -- Thanks for the comment, really good stuff to think about!

Now, I actually think one of the advantages here is to have an example that is about something other than testing a population mean. One of the things I struggle with in the introductory class is in trying to communicate that the concepts of confidence-intervals and hypothesis-tests apply to a whole universe of parameters other than just a mean (median, standard deviation, proportion, odds ratio, etc.) So dealing with those general concepts in isolation, prior to introducing the machinery of means-testing, I think might give valuable added perspective.

And I think that part of the demonstration is that somehow you do indeed have to categorize all possible sampling results under the null-hypothesis. For this brief example, you can list them individually. For the case of a mean from an unknown population, the analogy is to use the Central Limit Theorem, and conclude that they are at least approximately normally distributed (for a sufficiently large sample). So there is a correspondence there that I'm consciously trying to highlight.

I'm struggling with your suggested demonstrati...

2012-01-01T17:31:00.105-05:00

I'm struggling with your suggested demonstration, and I think it's because you mention hypothesis testing for means and then proceed with a demonstration that isn't about means. Also, I don't think it's as simple as defining a population then considering all the possible samples. That might make for an effective demonstration, but (as I understand it) hypothesis testing is totally unaware of the size of the population (i.e., your samples of 3 cards do not "know" they're sampling a population of 4 cards). By trying to define all possible samples, I fear students might be misled about the population-sample relationship in hypothesis testing and the theoretical nature of a sampling distribution.

I'm glad you're making me thing about this, because in my limited experience I haven't used much to explain the concept other than drawings of overlapping sampling distributions, and the general explanation that lots of overlap would be higher p-values, and little overlap would be small p-values. I'm guessing there might be some computer simulations that would be helpful, but I haven't explored enough (yet) to find them.