I think I'm right in thinking I should use a chi-squared contingency table test here because the data is nominal, but it seems to over-simplify my results and doesn't really test my hypothesis.

Basically, for my final year biology project i artificially pollinated some flowers under three different conditions. I want to find out if there is a significant difference between the frequencies of successfully fertilized flowers under the different conditions. The null hypothesis is that there is no difference at all; a 100% success rate in all flowers.

My observed value table looks like this:

---------------Successful--------Unsuccessful

Condition A -------11-----------------37

Condition B ------44 -----------------4

Condition C ------45 -----------------3

So I guess my expected value table would be:

---------------Successful--------Unsuccessful

Condition A -------48-----------------0

Condition B ------48 -----------------0

Condition C ------48 -----------------0

But i read somewhere that expected values can never be less than 1?

so i used the normal way of calculating expected values (as Minitab does) and I got a significant result...

but clearly the frequencies in condition B and condition C are NOT significantly different from each other, and the overall result is being swayed by condition A.

So (yup, there's more!) I was advised to break the test down and do A v.B, B v. C, A v. C, but i'm aware that this is a pretty crude way to carry out the test. Plus I'm still not sure it's really gonna tell me what I want to know...

Is there some way I could find out if the frequencies are significantly different from each other?

ANY help would be much appreciated!

thanks

x