Suppose I have repeated measurements of the same sample done by different people. Each person has different number of repeats. Here are the measurement data:
Person #1: 2, 3, 2
Person #2: 3, 3, 6
Person #3: 2, 5, 6, 4
Person #4: 2, 3, 4, 5
Person #5: 3, 2
I can group all these data together, and get the mean 3.4375, and its corresponding standard error $s/\sqrt{16}$ where s is just the standard deviation of all the data. The standard error turns out to be 0.353.
Another way to estimate the mean for this data set is: first calculate the mean for each person $\bar{x}_i$, and then pool the means $\Sigma_i n_i\bar{x}_i/\Sigma_i n_i$. This of course gives me the same mean at 3.4375. But if I estimate the standard error of mean using this pooled mean formula, it would be $\sqrt{\Sigma_i n_i^2var(\bar{x}_i)/(\Sigma_in_i)^2}$ where $var(\bar{x}_i)=var(x_i)/n_i$. But this gives me a standard error of 0.338.
Shouldn't I expect the standard errors to be the same using these two different mean calculation methods?