# theoretical confidence interval depending on sample size [closed]

I am using R and plain English to express my question. Let us say I have a "true"/made up population, which is normally distributed with a mean of 500000 and a standard deviation of 13000:

mean <- 500000
sd <- 13000
population <- rnorm(10000000, mean=mean, sd=sd)


I can then repeatedly sample from this population using different samples sizes ranging from 3 to 150. For each sample, I also calculate the 95% confidence interval using a t distribution. I think this is safe as at the beginning my sample size is pretty low (i.e. 3). This is not really exploited here but I think this would be correct?

results <- NULL

for (sample_size_ in 3:150) {

for (sample_number_ in 1:10) {

sample_ <- sample(population, sample_size_)
ci_test <- t.test(sample_, conf.level=0.95)

df <- data.frame(
sample_size = sample_size_
, low_ci = ci_test$$conf.int[] , up_ci = ci_test$$conf.int[]
, mean = mean(sample_)
)

results <- rbind(results, df)

}
}


I can then plot the data frame, which nicely shows how the sample means gets closer to the population mean (red dotted line) as the sample size increases:

ggplot(results, aes(y = mean, x = sample_size)) +
geom_point() +
geom_hline(yintercept=mean, linetype="dotted", color = "red", size=3)


This is the output (the actual code uses a theme): I guess this is straightforward but how can I determine these blue lines expressing the 95% theoretical uncertainty of the sample mean being inside the blue area? Is there a formula and does it depend on the knowledge of that the true distribution is normal? I guess it should not, as the sample means will be normally distributed according to the CLT?

Thanks.

PS:

This is the current/final code:

mu_ <- 500000
sd_ <- 13000
z_value <- qnorm(.975)
max_sample_size = 500
repeats = 2

results <- NULL
upper_and_lower <- NULL

for (sample_size_ in 3:max_sample_size) {

for (sample_number_ in 1:repeats) {

sample_ <- rnorm(sample_size_, mean=mu_, sd=sd_)
ci_test <- t.test(sample_, conf.level=0.95)

df <- data.frame(
sample_size = sample_size_
, low_ci = ci_test$$conf.int[] , up_ci = ci_test$$conf.int[]
, mean = mean(sample_)
)

results <- rbind(results, df)

}

df <- data.frame(
sample_size = sample_size_
, upper = mu_ + (z_value * (sd_/sqrt(sample_size_)))
, lower = mu_ - (z_value * (sd_/sqrt(sample_size_)))
)
upper_and_lower <- rbind(upper_and_lower, df)
}

ggplot() +
geom_point(data=results, aes(y = mean, x = sample_size)) +
geom_line(data=upper_and_lower, aes(y = upper, x = sample_size), color = "blue", size=2) +
geom_line(data=upper_and_lower, aes(y = lower, x = sample_size), color = "blue", size=2)


Resulting graph with theoretical 95% uncertainty band: • Even though you 'know' $\sigma = 13000$ (from simulation, you're pretending all you know for each t CI is $n, \bar X_n, S_n.$ Then 95% CIs from t.test in R will be about $50000 \pm 2 S_n/\sqrt{n}$ for $n > 30.$ For smooth curves, maybe use upper curve $50000 + 2(13000)/\sqrt{n}$ from $n = 30$ to $150.$ [Lower curve with $-.$] Dec 27, 2020 at 17:03
• Unrelated to the question, watch out when you define variables with names used by functions. When I do simulations like this and define mean and standard deviation parameters, I call them “mu” and “s” (should be “sigma”, but I’m content to call it “s”).
– Dave
Dec 27, 2020 at 17:41
• The blues lines that you imagine are not confidence intervals. Confidence intervals are a function of the data/sample mean $\bar{X}$, data/sample deviation $S$ and sample size $n$ and a constant $c$ which depends on the desired confidence level$$\bar{X}_n\pm c \frac{S_n}{\sqrt{n}}$$Your plot demonstrates how the distribution of $\bar{X}_n$ has a variance that scales with $n^{-0.5}$, and you could make boundaries that contain some $x\%$ of the observations, but that is not the same concept as confidence intervals Dec 29, 2020 at 16:28
• @cs0815 The confidence interval is $\bar{X}_n\pm c \frac{S_n}{\sqrt{n}}$. It depends on the sample/observation and you can not plot it as values that are for some given $n$ fixed. (But maybe you are not looking for a confidence interval, and instead you look for a prediction interval or tolerance interval?) Dec 29, 2020 at 17:16
• @cs0815 $c$ is a parameter that depends on the percentage for the confidence region. It will also depend on $n$. See for more information the link that I referenced in the comment. Jan 1, 2021 at 17:16

Nice experiment. The blue lines will be at $$\mu \pm z_{\alpha/2} \sigma/\sqrt{n}$$ where $$\alpha = 0.05$$ and $$\alpha \mapsto z_\alpha$$ is the upper quantile function of the standard normal and $$n$$ is sample_size_. In this case, this is exact since you are generating from the normal distribution and the sample mean will have the distribution $$N(\mu, \sigma^2/n)$$. In general, it holds approximately based on the CLT for large $$n$$.

If you are trying to interpret the CI, that would be a bit different. Putting a CI around each of the points vertically, for each sample_size_, roughly 95% of those intervals will cross the dotted red line (you cover the true mean $$\approx$$ 95% of the time, hence the coverage probability is 0.95). Since your replication size is small (10), this is not that accurate. Try increasing sample_number_ to say 1000 to see it better. What you will observe is that although the length of the CIs will be smaller as you increase $$n$$, still $$\approx$$ 95% of them keep covering the red line no matter what $$n$$ is.

PS. I am assuming that instead of these two lines:

population <- rnorm(10000000, mean=mean, sd=sd)
sample_ <- sample(population, sample_size_)


you would do something like this:

sample_ <- rnorm(sample_size_, mean=mean, sd=sd)


That is, sample from the normal distribution, not a finite population drawn from the normal distribution as you are doing here, although the results will be close in your case (unless your sample size starts approaching 10000000).

• Thanks. Reg PS yes. I just pretended that I have a fixed population. Dec 27, 2020 at 19:15
• Is the standard deviation in your formula the population one? I guess so? Dec 27, 2020 at 19:18
• @cs0815, yes, $\mu$ and $\sigma$ are the population mean and s.d. Dec 27, 2020 at 19:19
• @cs0815, no problem. Yes, it looks good to me. Dec 27, 2020 at 21:52
• Thanks for your help Dec 27, 2020 at 21:54