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I was thinking about a very basic matter, and arrived at the conclusion I know a lot less about it than I thought.

When people have data $ X_1^N $ sampled i.i.d. from a distribution: $ X_1^N \sim X $, it is customary to estimate the mean by $ \bar{X} = \frac{1}{N} \sum_{i=1}^N X_i $ and to estimate the confidence interval for the true mean $ \mu $ in the 95% confidence interval:

$$ \mu \in \left[\bar{X} - 1.96 \cdot \frac{\hat{std}(X)}{\sqrt{N}}, \bar{X} + 1.96 \cdot \frac{\hat{std}(X)}{\sqrt{N}}\right] $$

Where $ \hat{std}(X) $ is some estimate for the standard deviation of X.

Now, this comes from an assumption that X follows a normal distribution. Otherwise, this confidence interval is only valid asymptotically.

But, if the variable $ X $ belongs to $ \mathcal{L}^2 $, that is, if the standard deviation exists and is finite, could we derive a precise, non-asymptotic interval?

The answer is yes. By Markov inequality,

$$ \mathbf{P} \left[ \left| \frac{1}{N} \sum_{i=1}^N X_i - \mu \right| \gt \epsilon \right] \leq \frac{ \mathbf{P} \left[ \left( \frac{1}{N} \sum_{i=1}^N X_i - \mu \right)^2 \right] }{\epsilon^2} $$ $$ = \frac{Var(X)}{N \epsilon^2} $$

So if we want a 95% confidence interval, (that is, $ \mathbf{P} \left[ \left| \frac{1}{N} \sum_{i=1}^N X_i - \mu \right| \gt \epsilon \right] \leq 0.05 $), we might take:

$$ \epsilon = \sqrt{ \frac{Var(X)}{0.05 N}} \sim 4.47 \frac{std(X)}{\sqrt{N}} $$

So, if we want an exact (except by the error in $ \hat{std(X)} $), non-asymptotic interval for $ \mu $, instead of the one above, we should do:

$$ \mu \in \left[\bar{X} -4.47 \cdot \frac{\hat{std}(X)}{\sqrt{N}}, \bar{X} + 4.47 \cdot \frac{\hat{std}(X)}{\sqrt{N}}\right] $$

Is it a "bad" non-asymptotic confidence interval? That is, is it possible to find a better one, valid for any distribution in $ \mathcal{L}^2 $?

I imagine there could be a multiplicative factor that is a function of N, such that the number 4.47 progressively turns into 1.96 while N grows. But, for a variable in $ \mathcal{L}^2 $, I am not aware of any better bound than the one coming from Markov's inequality.

Thanks!

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