# Variance of sample variance?

What is the variance of the sample variance? In other words I am looking for $\mathrm{Var}(S^2)$.

I have started by expanding out $\mathrm{Var}(S^2)$ into $E(S^4) - [E(S^2)]^2$

I know that $[E(S^2)]^2$ is $\sigma$ to the power of 4. And that is as far as I got.

• Your expressions are very difficult to read. You need to edit and present your question in a better way. Oct 16, 2011 at 4:44
• One way of expressing $Var(S^2)$ is given on the Wikipedia page for variance. Oct 16, 2011 at 4:50
• It doesn't show how they derived it. Oct 16, 2011 at 4:51
• The solution to the question is in many books. You can easily find it. Oct 16, 2011 at 5:17
• There is a derivation on MathWorld's Sample Variance Distribution page. They use the "divide by $N$" convention rather than the "divide by $N-1$" convention, though, so you might have to adjust for that. Oct 16, 2011 at 5:39

Here's a general derivation that does not assume normality.

Let's rewrite the sample variance $S^2$ as an average over all pairs of indices: $$S^2={1\over{n\choose 2}}\sum_{\{i,j\}} {1\over2}(X_i-X_j)^2.$$ Since $\mathbb{E}[(X_i-X_j)^2/2]=\sigma^2$, we see that $S^2$ is an unbiased estimator for $\sigma^2$.

The variance of $S^2$ is the expected value of $$\left({1\over{n\choose 2}}\sum_{\{i,j\}} \left[{1\over2}(X_i-X_j)^2-\sigma^2\right]\right)^2.$$

When you expand the outer square, there are 3 types of cross product terms $$\left[{1\over2}(X_i-X_j)^2-\sigma^2\right] \left[{1\over2}(X_k-X_\ell)^2-\sigma^2\right]$$ depending on the size of the intersection $\{i,j\}\cap\{k,\ell\}$.

1. When this intersection is empty, the factors are independent and the expected cross product is zero.

2. There are $n(n-1)(n-2)$ terms where $|\{i,j\}\cap\{k,\ell\}|=1$ and each has an expected cross product of $(\mu_4-\sigma^4)/4$.

3. There are ${n\choose 2}$ terms where $|\{i,j\}\cap\{k,\ell\}|=2$ and each has an expected cross product of $(\mu_4+\sigma^4)/2$.

Putting it all together shows that $$\mbox{Var}(S^2)={\mu_4\over n}-{\sigma^4\,(n-3)\over n\,(n-1)}.$$ Here $\mu_4=\mathbb{E}[(X-\mu)^4]$ is the fourth central moment of $X$.

• a related question on stats.SE asks provides a different solution, and asks for a reference, your input would be appreciated: stats.stackexchange.com/q/29905/2750
– Abe
Jun 6, 2012 at 16:56
• @Abe Sorry, I don't have any references or worthwhile input. The above is a solution that I made up to teach my students.
– user940
Jun 6, 2012 at 19:07
• thanks, an answer to the stats.SE question solved my confusion: the discrepancy was use of kurtosis ($\mu_4$, the fourth central moment) vs excess kurtosis ($\kappa = \frac{\mu_4}{\sigma^4} -3$); one reference is Mood Graybill and Boes, 1974, Introduction to the Theory of Statistics
– Abe
Jun 6, 2012 at 22:39
• @ByronSchmuland It's probably too basic, but I have problems with the first expression of variance as a pair of indices. Is there any way you can send a reference for this equation? Ty Aug 3, 2015 at 14:21
• In the derivation, how do we see claims 2 and 3, i.e. that the expected value of $$\left[{1\over2}(X-Y)^2-\sigma^2\right] \left[{1\over2}(X-Y)^2-\sigma^2\right]$$ is $(\mu_4+\sigma^4)/2$, for X,Y i.i.d? Jul 28, 2017 at 11:35

Maybe, this will help. Let's suppose the samples are taking from a normal distribution. Then using the fact that $\frac{(n-1)S^2}{\sigma^2}$ is a chi squared random variable with $(n-1)$ degrees of freedom, we get \begin{align*} \text{Var}~\frac{(n-1)S^2}{\sigma^2} & = \text{Var}~\chi^{2}_{n-1} \\ \frac{(n-1)^2}{\sigma^4}\text{Var}~S^2 & = 2(n-1) \\ \text{Var}~S^2 & = \frac{2(n-1)\sigma^4}{(n-1)^2}\\ & = \frac{2\sigma^4}{(n-1)}, \end{align*}

where we have used that fact that $\text{Var}~\chi^{2}_{n-1}=2(n-1)$.

Hope this helps.

• Remember that $(n-1)S^2/\sigma^2$ is only guaranteed to be $\chi^2$ when the sample is taken from a normal distribution, though. Oct 16, 2011 at 6:09
• The question posed is a general one, whereas the answer is distribution-specific. Not appropriate, I am afraid. Apr 26, 2013 at 7:49
• @afsdfdfsaf Perhaps, you should ask that as a separate question.
– Nana
Apr 12, 2014 at 18:48
• The answer is extremely useful, but would have been even more useful if someone could reference why (n−1)S2/σ2 is a Chi squared Mar 9, 2015 at 7:06
• @moldovean About as to why $(n−1)S^2/\sigma^2$ is a Ki2 distribution, I see it this way : $\sum(x_i-\overline{x})^2$ is the sum of the square value of N variables following normal distribution with expected value 0 and variance $\sigma^2$. Then, since all the $(x_i-\overline{x})/\sigma^2$ follow a normal standard distribution, $Y = \sum^N((x_i-\overline{x})/\sigma)^2 = \frac{1}{\sigma^2}\sum^N(x_i-\overline{x})^2 = \frac{(n-1)S^2}{\sigma^2}$ follows a ki2 with N degrees of freedom, and not with N-1 degrees of freedom. I don't know what I am missing... Sep 27, 2019 at 10:01

There can be some confusion in defining the sample variance ... 1/n vs 1/(n-1). The OP here is, I take it, using the sample variance with 1/(n-1) ... namely the unbiased estimator of the population variance, otherwise known as the second h-statistic:

h2 = HStatistic[2][[2]]

These sorts of problems can now be solved by computer. Here is the solution using the mathStatica add-on to Mathematica. In particular, we seek the Var[h2], where the variance is just the 2nd central moment, and express the answer in terms of central moments of the population:

CentralMomentToCentral[2, h2]

We could just as easily find, say, the 4th central moment of the sample variance, as:

CentralMomentToCentral[4, h2]

• Does your program also let you handle dependent random variables? Presumably, then the result would be in terms of higher-order covariances. Aug 15, 2019 at 15:17
• Yes - it works for dependent random variables too. There is a multivariate example at: math.stackexchange.com/questions/589865/… Aug 16, 2019 at 21:54
• +1 Please, take a look at my new question: math.stackexchange.com/questions/4640597/… Feb 16, 2023 at 19:43

This is quite a well-known result in statistics, and it can be found in a number of books and papers on sampling theory. You can find a range of useful moment results of this kind in O'Neill (2014) (this one is given in Result 3, p. 284). Consider a distribution with mean $$\mu$$, variance $$\sigma^2$$, skewness $$\gamma$$ and kurtosis $$\kappa$$ (where all these moments are finite).$$^\dagger$$ Taking $$n$$ IID draws from this distribution and taking the variance of the sample variance $$S_n^2$$ gives:

$$\boxed{\mathbb{V}(S_n^2) = \bigg( \kappa - \frac{n-3}{n-1} \bigg) \frac{\sigma^4}{n}}$$

In the special case where the underlying distribution is mesokurtic (e.g., for a normal distribution) we have $$\kappa = 3$$ and this expression then reduces to:

$$\mathbb{V}(S_n^2) = \frac{2n}{n-1} \cdot \frac{\sigma^4}{n} = \frac{2 \sigma^4}{n-1}.$$

You might also be interested to note that, in general, the sample variance and sample mean are correlated. Their covariance is $$\mathbb{Cov}(\bar{X}_n, S_n^2) = \gamma \sigma^3/n$$ and their corresponding correlation coefficient is:

\begin{align} \mathbb{Corr}(\bar{X}_n, S_n^2) &= \frac{\mathbb{Cov}(\bar{X}_n, S_n^2)}{\mathbb{S}(\bar{X}_n) \cdot \mathbb{S}(S_n^2)} \\[6pt] &= \frac{\gamma \sigma^3}{n} \Bigg/ \frac{\sigma}{\sqrt{n}} \cdot \sqrt{ \Big( \kappa - \frac{n-3}{n-1} \Big) \frac{\sigma^4}{n}} \\[6pt] &= \frac{\gamma \sigma^3}{n} \Bigg/ \frac{\sigma^3}{n} \cdot \sqrt{\kappa - \frac{n-3}{n-1}} \\[6pt] &= \frac{\gamma}{\sqrt{\kappa - (n-3)/(n-1)}}, \\[6pt] \end{align}

and as $$n \rightarrow \infty$$ you get:

$$\mathbb{Corr}(\bar{X}_n, S_n^2) \rightarrow \frac{\gamma}{\sqrt{\kappa - 1}},$$

which is the adjusted skewness of the underlying distribution. You can find further discussion of moments of the sample moments (including correlation between them) in O'Neill (2014).

$$^\dagger$$ Actually, you can just assume that the kurtosis is finite, and this implies that all the lower-order moments are also finite.

• Thanks for clarifying and bringing the reference O´Neill (2014). Very useful. It is a common mistake taking the particular formula for $V(S_n^2)$, from a Normal sample, as the general formula. And thanks again for the bonus formula for the correlation between $\bar X_n$ and $S_n^2$. I knew they were not independent in general but have never seen this formula before. Aug 30, 2022 at 3:02
• @bluemaster: Yes, that is a common mistake, not just in this particular case but in many other contexts too. There are a number of general moment formulae in statistics that reduce down to special cases when you use a normal distribution (taking $\gamma = 0$ and $\kappa=3$). It is fairly common that people unfamiliar with the field will use the formulae for the special cases without being aware that the general formulae depend on skewness and kurtosis.
– Ben
Aug 30, 2022 at 23:13

Showing the derivation of $$E(\left[{1\over2}(X-Y)^2-\sigma^2\right]^2) = (\mu_4+\sigma^4)/2$$ of user940:

LHS:

$$E(\left[{1\over2}(X-Y)^2-\sigma^2\right]^2) = E(\frac{1}{4}(X-Y)^4 - (X-Y)^2 \sigma^2 + \sigma^4) = E(\frac{1}{4}(X-Y)^4) - 2\sigma^2\sigma^2 + \sigma^4 = E(\frac{1}{4}(X-Y)^4) - \sigma^4 = \frac{1}{4}E(X^4 -4X^3Y +6X^2Y^2 -4XY^3 + Y^4) -\sigma^4 = \frac{1}{4}(2E(X^4) -8E(X)E(X^3) +6 E(X^2)(X^2)) - \sigma^4 = \frac{1}{2}(E(X^4)-4E(X)E(X^3) +3 E(X^2)(X^2) - 2\sigma^4)$$

I use the fact that $$E((x-y)^2) = 2\sigma^2$$ here.

RHS:

$$\require{cancel} (\mu_4+\sigma^4)/2 = \frac{1}{2}(E((X-\mu)^4) + \sigma^4) = \frac{1}{2}(E((X-E(X))^4) + \sigma^4) = \frac{1}{2}(E(X^4 -4X^3E(X) + 6X^2E(X)^2 -4XE(X)^3 + E(X)^4) + \sigma^4) = \frac{1}{2}(E(X^4 -4X^3E(X) + 6X^2E(X^2) - 6X^2\sigma^2 -4XE(X)(E(X^2)-\sigma^2) + (E(X^2)-\sigma^2)^2) + \sigma^4) = \frac{1}{2}(E(X^4) -4E(X)^3E(X) + 6E(X)^2E(X^2) - 6E(X)^2\sigma^2 -4E(X)^2(E(X^2)-\sigma^2) + (E(X^2)-\sigma^2)^2 + \sigma^4) = \frac{1}{2}(E(X^4) -4E(X)^3E(X) + 6E(X)^2E(X^2) - \cancel{6E(X)^2\sigma^2} -4E(X^2)E(X^2) +\cancel{4E(X^2)\sigma^2 +4E(X^2)\sigma^2} - 4\sigma^4 + E(X^2)^2-\cancel{2E(X^2)\sigma^2} + \sigma^4 + \sigma^4) = \frac{1}{2}(E(X^4) -4E(X)^3E(X) + 3E(X)^2E(X^2) - 2\sigma^4)$$

I use the fact that $$E(x) = \mu$$ and that $$E(x)^2 = E(x^2) - \sigma^2$$

Now LHS = RHS.

I know that this question is very old but wanted to contribute nonetheless as I found it very hard to find a proof online that was satisfying enough, yet easy enough to follow. Proofs by induction are not constructive and do not tell you where the expression comes from in the first place.

While user940's answer is helpful, some parts are not very obvious, especially the first line and the later lines involving counting.

I have come up with a solution that is easier to follow and intuit, and I hope it helps others.

Let $$\mu := \mathop{\mathbb{E}}\left(X_{i}\right)$$ denote the population mean and $$\mu_{k} := \mathop{\mathbb{E}}\left[(X_{i} - \mu)^{k}\right]$$ denote the $$k$$th centered population moment. Notice that with this notation, $$\mu_{2}$$ is the population variance (what you are used to notating with $$\sigma^2$$, so $$\mu_{2}^{2}$$ would be the square of this (or $$\sigma^{4}$$).

Then, if the kurtosis exists, \begin{align} \boxed{\mathop{\mathrm{Var}}\left(S^{2}\right) = \frac{1}{n}\left(\mu_{4} - \frac{n - 3}{n - 1}\mu_{2}^{2}\right).} \end{align}

Beginning from the definition of sample variance: $$S^{2} := \frac{1}{n - 1} \sum_{i = 1}^{n} (X_{i} - \bar{X})^{2},$$ let us derive the following useful lemma:

Lemma (reformulation of $$S^{2}$$ as the average distance between two datapoints). Let $$\mathbf{X}$$ be a sample of size $$n$$ and $$S^{2}$$ be the sample variance. Then $$S^{2} \equiv \frac{1}{2n (n - 1)} \sum_{i=1}^{n}\sum_{j=1}^{n} (X_{i} - X_{j})^{2}.$$

Proof. Pick some $$X_{j}$$ and note that: \begin{align} S^{2} &\equiv \frac{1}{n- 1} \sum_{i=1}^{n}(X_{i} - X_{j} + X_{j} - \bar{X})^{2} \\ \implies (n - 1)S^{2} &= \sum_{i=1}^{n}(X_{i} - X_{j})^{2} + 2 \sum_{i=1}^{n} (X_{i} - X_{j})(X_{j} - \bar{X}) + \sum_{i=1}^{n}(X_{j} - \bar{X})^{2}. \end{align} Now sum this over $$j$$: \begin{align} n(n - 1)S^{2} &= \sum_{j=1}^{n}\sum_{i=1}^{n}(X_{i} - X_{j})^{2} + 2 \sum_{j=1}^{n} \sum_{i=1}^{n} (X_{i} - X_{j})(X_{j} - \bar{X}) + \sum_{j=1}^{n} \sum_{i=1}^{n}(X_{j} - \bar{X})^{2}. \end{align} The final term is simply $$n(n - 1)S^{2}$$ again, so we have: \begin{align} \sum_{i=1}^{n}\sum_{j=1}^{n}(X_{i} - X_{j})^{2} &= - 2 \sum_{i=1}^{n} \sum_{j=1}^{n} (X_{i} - X_{j})(X_{j} - \bar{X}) \\ &= 2 \sum_{i=1}^{n} \sum_{j=1}^{n} (X_{j} - \bar{X} + \bar{X} - X_{i})(X_{j} - \bar{X}) \\ &= 2 \sum_{i=1}^{n} \sum_{j=1}^{n} (X_{j} - \bar{X})^{2} + 2 \sum_{i=1}^{n} (\bar{X} - X_{i}) \underbrace{\sum_{j=1}^{n} (X_{j} - \bar{X})}_{0} \\ &= 2n(n - 1)S^{2}, \end{align} giving the result.

Using this Lemma, we can now find the sample variance. Let's begin by using a small trick: Let $$Z_{i} := X_{i} - \mu$$. This means that $$\mathop{\mathbb{E}}\left(Z_{i}^{k}\right) = \mu_{k}$$ gives you the $$k$$th central moment. This makes the algebra much easier.

Proceed as so: \begin{align} S^{2} &\equiv \frac{1}{2n(n - 1)}\sum_{i=1}^{n}\sum_{j=1}^{n} \left( \overbrace{(X_{i} - \mu)}^{Z_{i}} - \overbrace{(X_{j} - \mu)}^{Z_{j}}\right)^{2} \\ \implies \mathop{\mathrm{Var}}\left(S^{2}\right) &\equiv \mathop{\mathrm{Var}}\left[\frac{1}{2n(n - 1)} \left(\sum_{i=1}^{n} \sum_{j=1}^{n} \left(Z_{i}^{2} + Z_{j}^{2} - 2 Z_{i} Z_{j}\right)\right) \right] \\ &= \mathop{\mathrm{Var}}\left[\frac{1}{2n(n - 1)} \left(2n \sum_{i=1}^{n} Z_{i}^{2} - 2 \sum_{i=1}^{n}\sum_{j=1}^{n}Z_{i}Z_{j}\right)\right] \\ &= \mathop{\mathrm{Var}}\left[\frac{1}{n(n - 1)}\left(n \sum_{i=1}^{n} Z_{i}^{2} - \sum_{i=1}^{n}\sum_{j=1}^{n}Z_{i}Z_{j}\right)\right] \\ &= \frac{1}{n^{2}(n - 1)^{2}} \Bigg[ n^{2} \mathop{\mathrm{Var}}\left(\sum_{i=1}^{n}Z_{i}^{2}\right) + \mathop{\mathrm{Var}}\left(\sum_{i=1}^{n}\sum_{j=1}^{n} Z_{i} Z_{j}\right)\\ &\phantom{=} - 2 n \mathop{\mathrm{Cov}}\left(\sum_{i=1}^{n}Z_{i}^{2}, \sum_{i=1}^{n}\sum_{j=1}^{n}Z_{i} Z_{j}\right)\Bigg]. \tag{$$\bigstar$$} \end{align}

Now we must calculate each of these variances and covariances. For the first one, \begin{align} \mathop{\mathrm{Var}}\left(\sum_{i=1}^{n}Z_{i}^{2}\right) &= n \mathop{\mathrm{Var}}\left(Z_{1}^{2}\right) \\ &= n\left(\mathop{\mathbb{E}}\left(Z_{1}^{4}\right) - \mathop{\mathbb{E}}\left(Z_{1}^{2}\right)^{2}\right) \\ &= n(\mu_{4} - \mu_{2}^{2}). \tag{$$\blacktriangle$$} \end{align} (Note that $$\mu_{2}^{2}$$ is the variance squared.)

For the second variance, \begin{align} \mathop{\mathrm{Var}}\left(\sum_{i=1}^{n}\sum_{j=1}^{n}Z_{i} Z_{j}\right) &= \mathop{\mathbb{E}}\left(\left(\sum_{i=1}^{n}\sum_{j=1}^{n} Z_{i}Z_{j}\right)^{2}\right) - \mathop{\mathbb{E}}\left(\sum_{i=1}^{n}\sum_{j=1}^{n} Z_{i}Z_{j}\right)^{2}. \end{align}

To find the first expectation, we write: \begin{align} \mathop{\mathbb{E}}\left(\left(\sum_{i=1}^{n}\sum_{j=1}^{n} Z_{i}Z_{j}\right)^{2}\right) &= \sum_{i=1}^{n}\sum_{j=1}^{n}\sum_{k=1}^{n}\sum_{l=1}^{n} \mathop{\mathbb{E}}\left(Z_{i}Z_{j}Z_{k}Z_{l}\right). \end{align}

To compute this, we must consider all the possible ways that the indices $$(i,j,k,l)$$ could be different. If there is any index that is different from all the other indices, then the expectation is zero. Thus, the only way for $$\mathop{\mathbb{E}}\left(Z_{i}Z_{j}Z_{k}Z_{l}\right)$$ to be non-zero is if all indices are the same, or if there are two distinct pairs of equal indices (e.g. $$i = j \neq k = l$$).

In the first case, the expectation becomes $$\mathop{\mathbb{E}}\left(Z_{i}^{4}\right) = \mu_{4}$$, and there are $$n$$ ways that this happens.

In the second case, suppose $$i = j$$ and $$k = l$$ and $$k > i$$ (We take one to be strictly larger to avoid double counting in what follows). Then the expectation becomes $$\mathop{\mathbb{E}}\left(Z_{i}^{2} Z_{k}^{2}\right) = \mathop{\mathbb{E}}\left(Z_{i}^{2}\right) \mathop{\mathbb{E}}\left(Z_{k}^{2}\right) = \mu_{2}^{2}$$. The number of ways for $$i = j > k = l$$ is $${n \choose 2} = \frac{1}{2}n(n - 1)$$. But there are other ways of choosing two pairs. In particular, there are $${4 \choose 2} = 6$$ ways of choosing two pairs of indices from 4 indices, giving a total of $$3n(n - 1)$$ ways that the expectation equals $$\mu_{2}^{2}$$. Thus, $$\mathop{\mathbb{E}}\left(\left(\sum_{i=1}^{n}\sum_{j=1}^{n} Z_{i}Z_{j}\right)^{2}\right) = n \mu_{4} + 3n(n - 1)\mu_{2}^{2}$$.

Now we must calculate the expectation $$\mathop{\mathbb{E}}\left(\sum_{i=1}^{n}\sum_{j=1}^{n}Z_{i}Z_{j}\right) = \sum_{i=1}^{n}\sum_{j=1}^{n}\mathop{\mathbb{E}}\left(Z_{i}Z_{j}\right)$$. Since $$Z_{i}$$ and $$Z_{j}$$ are independent, $$\mathop{\mathbb{E}}\left(Z_{i}Z_{j}\right) = 0$$ whenever $$i \neq j$$. Thus, the expectation becomes $$\mathop{\mathbb{E}}\left(\sum_{i=1}^{n}\sum_{j=1}^{n}Z_{i}Z_{j}\right) = \mathop{\mathbb{E}}\left(\sum_{i=1}^{n}Z_{i}^{2}\right) = n \mu_{2}$$.

Subtracting $$\mathop{\mathbb{E}}\left(\sum_{i=1}^{n}\sum_{j=1}^{n}Z_{i}Z_{j}\right)^{2} = n^{2}\mu_{2}^{2}$$ from the previous expectation, we have: \begin{align} \mathop{\mathrm{Var}}\left(\sum_{i=1}^{n}\sum_{j=1}^{n}Z_{i}Z_{j}\right) &= n \mu_{4} + 2 n^{2} \mu_{2}^{2} - 3n \mu_{2}^{2}. \tag{$$\spadesuit$$} \end{align}

Finally, we calculate the covariance: \begin{align} \mathop{\mathrm{Cov}}\left(\sum_{i=1}^{n}Z_{i}^{2}, \sum_{i=1}^{n}\sum_{j=1}^{n}Z_{i}Z_{j}\right) &= \mathop{\mathbb{E}}\left(\sum_{i=1}^{n}\sum_{j=1}^{n}\sum_{k=1}^{n}Z_{i}^{2}Z_{j}Z_{k}\right) - \mathop{\mathbb{E}}\left(\sum_{i=1}^{n}Z_{i}^{2}\right)\mathop{\mathbb{E}}\left(\sum_{i=1}^{n}\sum_{j=1}^{n}Z_{i}Z_{j}\right). \end{align}

We have already found that the final two expectations are equal to $$n \mu_{2}$$, so the second term becomes $$n^{2} \mu_{2}^{2}$$.

For $$\sum_{i=1}^{n}\sum_{j=1}^{n}\sum_{k=1}^{n} \mathop{\mathbb{E}}\left(Z_{i}^{2}Z_{j}Z_{k}\right)$$, notice that the expectation is $$0$$ whenever $$j \neq k$$. When $$i = j = k$$, the expectation is $$\mathop{\mathbb{E}}\left(Z_{1}^{4}\right) = \mu_{4}$$ and there are $$n$$ ways of achieving this. When $$i \neq j = k$$, the expectation is $$\mu_{2}^{2}$$ and there are $$n(n - 1)$$ ways of achieving this (now the order of $$i, j$$ matters, so we do double count).

We therefore get: \begin{align} \mathop{\mathrm{Cov}}\left(\sum_{i=1}^{n}Z_{i}^{2}, \sum_{i=1}^{n}\sum_{j=1}^{n}Z_{i}Z_{j}\right) &= n(\mu_{4} - \mu_{2}^{2}). \tag{$$\clubsuit$$} \end{align}

Substituting $$\clubsuit, \spadesuit, \blacktriangle$$ the values we just found into $$\bigstar$$, we get \begin{align} \mathop{\mathrm{Var}}\left(S^{2}\right) &= \frac{1}{n^{2}(n - 1^{2})} \left[(n^{3} - 2n^{2} + n)\mu_{4} - (n^{3} - 4n^{2} + 3n) \mu_{2}^{2}\right] \\ &= \frac{1}{n^{2}(n - 1)^{2}} \left[n(n - 1)^{2} \mu_{4} - n(n - 1)(n - 3)\mu_{2}^{2}\right] \\ &= \frac{1}{n} \left(\mu_{4} - \frac{n - 3}{n - 1} \mu_{2}^{2}\right) \end{align}

I will use this as an example of this theorem(from Seber, G.A. and Lee, A.J. (2012))
Let $$X_1, X_2, ... , X_n$$ be independent rvs with means $$(\theta_1, \theta_2, ... ,\theta_n)$$,common $$\mu_2,\mu_3,\mu_4$$. If A is any n x n symmetric matrix and $$a$$ is a column vector of the diagonal elements of A, then
$$var[X'AX]=(\mu_4-3\mu^2_2)a'a+2\mu^2_2tr(A^2)+4\mu_2\theta'A^2\theta+4\mu_3\theta'Aa$$ denote $$1_n$$ as n-dim column vector that all elements are 1, notice that for sample variance $$S^2=\frac{1}{n-1}X'AX, where A=I_n-\frac{1}{n}1_n1_n'$$ and we have$$A^2=A$$, $$a=(1-\frac{1}{n})1_n$$
since $$X_i$$ in our case are iid, let's say their mean is $$\mu$$, then $$\theta=\mu1_n$$
so the third and fourth term is $$0$$,since $$A^2\theta=A\theta=\mu(1_n-\frac{1}{n}1_n(1_n'1_n))=0\\ Aa=(1-\frac{1}{n})(1_n-\frac{1}{n}1_n(1_n'1_n))=0$$ then$$var[S^2]=\frac{1}{(n-1)^2}[(\mu_4-3\mu_2^2)(1-\frac{1}{n})^2n+2\mu_2^2(n-1)]=\frac{\mu_4}{n}-\frac{n-3}{n(n-1)}\mu_2^2$$