Hi Kaxt,

I think it's what you say, but I'll try to clarify it to make sure.

I have four separate data sets, and each data set consists of something like this:

"A mutant cross yielded 401 males and 352 females, and a wildtype cross yielded 704 males and 680 females."

Clearly I can combine all four sets of numbers to generate a single contingency table, but it seems like there would be some test that people would run to tell you that the result you're getting isn't an artifact of something having been weird in one of your data sets. In the extreme, my numbers could have been:

1. mutant: 50 males and 50 females; wildtype: 50 males and 50 females

2. mutant: 50 males and 50 females; wildtype: 50 males and 50 females

3. mutant: 50 males and 50 females; wildtype: 50 males and 50 females

4. mutant: 500 males and 0 females; wildtype: 50 males and 50 females

If I combine all of those into a single contingency table, I'll find that the mutant cross gives more males than females, but clearly it's just because there's something funny about the fourth data set. It seems to me like there will be situations where you want to combine several data sets to increase your statistical power, but you want some sort of test that says, "It's OK that I combined them all, because none of these data sets looks like an outlier."

Does that make sense?

Thanks.

Eric