For a random variables $X_1,X_2,\ldots, X_n$, the law of large numbers says that the average of the results obtained from a large number of trials should be close to the expected value and tends to become closer to the expected value as more trials are performed, i.e.,


where $\bar{X}$ is the expected value.

But the same doesn't hold for the following:


In fact, $(2)$ tends to increase in absolute value as $n$ increases.

I am finding it very surprising and hard to understand. Because I expected that if the average tends to expected value, then the sum should tend to $n\times$expected value.

Let $Y=\sum_{i=1}^nX_i-n\bar{X}$, then


The way I try to convice myself is as follows: when $n\rightarrow\infty$, the LHS of $(3)$ tends to zero, thus $(1)$ will hold. But $(2)$ will not converge to zero, because the sum of the fluctuations around the expected value sum up and increase, thus forming divergent series.

Any help on obtaining a better intuition behind the differences between $(1)$ and $(2)$ is appreciated.


1 Answer 1


I take it we are assuming $X_i$ are iid ($X_i \sim \mathbb{P}_X$) with mean $\mu_X$

To be a bit more precise on (1):

$$\lim_{n \to \infty} \frac1n \sum_1^n X_i = \mu_X\;\;a.s.$$

So the sample mean converges to the mean almost surely (or "with probability 1")

Key here is that the the sample mean forces the variance to go to $0$.

In contrast for $Y_n := \sum_1^n X_i - n\mu_X$ we get an unbounded variance:

$$V[Y_n] = \sum_1^n V[X_i] \xrightarrow{n\to \infty} \infty$$

While the mean stays at $0$:

$$E[X_i] = \sum_1^n \mu_X - n\mu_X = 0$$

So we have an increasingly variable process with stationary mean.

Therefore, the larger the value of $n$, the less likely we are to have $Y_n$ near $0$, so we cannot have $Y_n \to 0,w.p. 1$ (since if $X \not\xrightarrow{p} Y \implies X \not \xrightarrow{a.s.} Y$)

  • 1
    $\begingroup$ your explanation is to the point, thanks! $\endgroup$
    – Lee
    Aug 31, 2022 at 3:57
  • $\begingroup$ @Lee happy to help! $\endgroup$
    – Annika
    Aug 31, 2022 at 6:07

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