Imagine that you repeat an experiment three times. In each experiment, you collect triplicate measurements. The triplicates tend to be fairly close together, compared to the differences among the three experimental means. Computing the grand mean is pretty easy. But how can one compute a confidence interval for the grand mean?
Experiment 1: 34, 41, 39
Experiment 2: 45, 51, 52
Experiment 3: 29, 31, 35
Assume that the replicate values within an experiment follow a Gaussian distribution, as does the mean values of each experiment. The SD of variation within an experiment is smaller than the SD among experimental means. Assume also that there is no ordering of the three values in each experiment. The left-to-right order of the three values in each row is entirely arbitrary.
The simple approach is to first compute the mean of each experiment: 38.0, 49.3, and 31.7, and then compute the mean, and its 95% confidence interval, of those three values. Using this method, the grand mean is 39.7 with the 95% confidence interval ranging from 17.4 to 61.9.
The problem with that approach is that it totally ignores the variation among triplicates. I wonder if there isn't a good way to account for that variation.