That’s how a standard error with normal-ish data works. The more data points for the estimation of a conditional mean you have, the fewer of the data point will be within it. For a normal distribution, the SE=SD/√N . Heck, you can even just calculate which proportion of the distribution you can expect to be within the 95% CI as a function of sample size. (Its a bit more complicated because of how probabilities factor into this, but for a large enough N it’s fine)
For N=9, you’d expect 26% of data points within the 95% CI of the mean
For N=16, 19%
For 25, 16%
For 100, 8%
For 400, 4%
Etc
Out of curiosity: What issue did you take with the error margin not including most data points?
That’s how a standard error with normal-ish data works. The more data points for the estimation of a conditional mean you have, the fewer of the data point will be within it. For a normal distribution, the SE=SD/√N . Heck, you can even just calculate which proportion of the distribution you can expect to be within the 95% CI as a function of sample size. (Its a bit more complicated because of how probabilities factor into this, but for a large enough N it’s fine)
For N=9, you’d expect 26% of data points within the 95% CI of the mean For N=16, 19% For 25, 16% For 100, 8% For 400, 4% Etc
Out of curiosity: What issue did you take with the error margin not including most data points?
Oops, should have multiplied those intervals with 1.96, ao here again:
9 - 49%
16 - 38%
25 - 30%
100 -16%
400 - 8%