hey stats geeks...

[chhotii]

Let's say you have a situation in which you're tempted to use a t-test. However, not all the values came from different subjects. One subject was read 4 times, one subject was read 2 times, and the other 3 reads are each from a different subject. (Don't ask me why this was done, not my experiment...) This so totally violates the assumption of samples being independent, right? What would you do? Take an average for each subject, and do a t-test on the averages for each subject?

Comments

[quietann.livejournal.com]

argh, I didn't notice the only 5 people thing, which kind of rules out a t-test anyway.

If you had more people, and the 4 runs were "ordered" in some way, and knew, when a person had less than 4 runs which runs they did or did not have, *and* that the missing runs were "missing completely at random" or possibly "missing at random", and the data met a bunch of other assumptions, you might be able to move into the territory of "mixed effects modeling."

But barring that, f's approach would be good for poking around and exploring...

However.... I'm thinking from a hypothesis-testing point of view (because that's the stats world I live in the most) and I don't really see a hypothesis anywhere.... so I'd wonder just what hypothesis is being tested anyway.

(Oh, and if you had just one person doing the exp't over and over, you'd need to worry about carryover when setting up the pairs.... e.g. is the result of the 2nd pair influenced by the first, the third pair influenced by the first and second, etc.)