Maybe Im not getting what you mean

Well, I'm basing this on the assumption that the population average resistance is the nominal value.

If I take random samples from that population, the distribution of the averages of these samples will be normally distributed around the population average.

The average amount of variation in the samples will fall as sample size increases, eventually plateauing at the population variance.

What is more interesting is the variance of the average value as the sample size increases and whether or not we can say anything about the "sample" of (say) 100 consecutive resistors from a reel.

If you look at

this, and take a look at the data, you can see the issue.

400 1k 1% resistors averaged 999.72462 ohms. that's within 0.0227% of the nominal value. Now, who can say if the population value is 1000 ohms, 9999.72 ohms, or some other value -- since we can't measure the entire population. However this is a very good estimate (arguably, if there is long term drift it isn't)

2 values were outside +/- 0.5%, being 0.055% low.

If we take samples of these resistors, each being 100 resistors, and for simplicity, the first, second, third and fourth hundred resistors form the 4 samples) we find that their average is also a good predictor of the population value, and an even better predictor of the value for the sample of 400.

For these resistors, a sample of 100 looks like it would give me a value within 0.04% of the nominal population value.

If we were to assume that these results could be used to predict the value of other samples of different resistors, then 100 0.1% resistors might be expected to be within 0.004% of the nominal value.

So that's an observation, but lacking (currently) the ability to measure 0.1% resistors this accurately, I can't duplicate the measurements.

What I'm interested in is the mathematical prediction based on assumptions about the distribution and nominal value. My stats are not what they used to be