How to compare variables with data from multiple experiments? I have an experiment in which i compare the amount of attached bacteria in three different conditions. I have repeated this experimented three times, so now i have three datasets with the same data (identical in style, not in exact numbers). Now i would like to perform statistics to confirm an effect of a condition, and i could simply perform a t test to get that result. I could then do that three times, and have three p values, but i would rather have one test, analysing all the data at once, somehow correcting for the fact that some data points (within an experiment) are more related than other datapoints (between experiments).
Any help would be greatly appreciated!
I was thinking about a repeated measures ANOVA, but that does not work because i am not experimenting on the same samples three times, rather i have different samples each time.
If any more info is necessary i will be happy to provide it.

You could treat each repetition of your experiment as a i.e. you probably have a randomised complete block design. If you have replicated the experiment in each repetition then you can estimate block by the treatment interaction. You would also need to think whether the blocks should be treated as fixed or random factors.
