# Background on bias correction constants

The standard deviation is calculated like this:

$$SD = \left(\frac{1}{N-constant} \sum_{i=1}^N (x_i - \overline{x})^2\right)^{1/2}$$

Following Wikipedia's entry on the standard deviation, the biasedness of the estimation of a population SD given a sample depends on $constant$ in the following way:

• $correction=0$: just sample sd, heavily biased to be smaller than population sd.
• $correction=1$: Bessel's correction, less biased but still smaller.
• $correction=1.5$: "rule of thumb", the best single value for an unbiased estimate.

# Question

I simulated this and found that $\sqrt{2}$ is an even better value for $constant$, especially for small samples (n < 10) where $1.5$ overestimates population SD. Have I just discovered something fantastic or am I missing something here?

# Simulation

For each of the sample sizes $n=2,3,5,10,20,60,100,200$, I generated 3.000 samples using rnorm(n, 0, 15). For each sample size, I then estimated population SD using each of the above constants. Here's the result:

Each plot is a different estimation constant. The "error" in the title is mean(sd.estimations - sd.real). The red line is the real SD. The blue line shows the estimated sd. The vertical gray lines mark change in sample size. Points show individual sd-estimations.

It is clear that $\sqrt{2}$ is superior to $1.5$. This is true for large sample sizes as well, even though it's not clear from this plot. Here's the R script that generated these plots.

# Update and conclusion

$\sqrt{2}$ is close to the analytically correct solution but doesn't outperform it. It remains a heuristic which could be used out of laziness or for computational efficiency with small sample sizes.

Actually, the closest approximation depends on the sample size you want to calculate for. Here's a few optimal values for different sample sizes:

• $4 < n < 10$: $\sqrt{2.15} = 1.47$ deviates with 0.4% at most.
• $10 < n < 50$: $\sqrt{2.22} = 1.49$ deviates with 0.04 % at most.
• $40 < n < 300$: $\sqrt{2.2465} = 1.4988$ deviates with 0.0025% at most.

As sample size increases, the constant approaches the "1.5 rule of thumb". Thus the conclusion is that $\sqrt(2)$ is quick-and-dirty for small sample sizes. For larger samples, reasonable approximations can be made with 1.5.

And just to be clear: Bessel's correction is still the right way to get unbiased when estimating variance. The observations above only pertain to the estimation of the population standard deviation.

Maybe. What it appears that you did, is hit upon the $c_4(N)$ correction factor stated also in this wikipedia article. Specifically: You propose the estimator

$$\tilde s = \frac 1{\sqrt {N-2^{1/2}}}\cdot (S_x)^{1/2}$$ where $S_x$ is the sum of squared deviations from the mean

The article you mention defines (although not very clearly) the estimator

$$\hat s = \frac 1{\sqrt {N-1}}\cdot\left[\sqrt{\frac{2}{N-1}}\,\,\,\frac{\Gamma\left(\frac{N}{2}\right)}{\Gamma\left(\frac{N-1}{2}\right)}\right]^{-1} \cdot (S_x)^{1/2} = \frac {\Gamma\left(\frac{N-1}{2}\right)}{2^{1/2}\Gamma\left(\frac{N}{2}\right)}\cdot (S_x)^{1/2}$$

where

$$c_4(N) = \sqrt{\frac{2}{N-1}}\,\,\,\frac{\Gamma\left(\frac{N}{2}\right)}{\Gamma\left(\frac{N-1}{2}\right)}$$

Calculating the values of the two proposed multiplication factors we find \begin{array}{| r | r | r |} \hline N & \frac{1}{\sqrt{N-2^{1/2}}} & \frac{1}{c_4(N)\sqrt{N-1}} \\ \hline 3 & 0.7941 & 0.7979 \\ 4 & 0.6219 & 0.6267 \\ 5 & 0.5281 & 0.5319 \\ 6 & 0.467 & 0.47 \\ 7 & 0.4231 & 0.4255 \\ 8 & 0.3897 & 0.3917 \\ 9 & 0.3631 & 0.3647 \\ 10 & 0.3413 & 0.3427 \\ 11 & 0.323 & 0.3242 \\ 12 & 0.3074 & 0.3084 \\ 13 & 0.2938 & 0.2947 \\ 14 & 0.2819 & 0.2827 \\ 15 & 0.2713 & 0.2721 \\ 16 & 0.2618 & 0.2625 \\ 17 & 0.2533 & 0.2539 \\ 18 & 0.2455 & 0.2461 \\ 19 & 0.2385 & 0.239 \\ 20 & 0.232 & 0.2325 \\ 21 & 0.226 & 0.2264 \\ 22 & 0.2204 & 0.2208 \\ 23 & 0.2152 & 0.2156 \\ 24 & 0.2104 & 0.2108 \\ 25 & 0.2059 & 0.2063 \\ 26 & 0.2017 & 0.202 \\ 27 & 0.1977 & 0.198 \\ 28 & 0.1939 & 0.1942 \\ 29 & 0.1904 & 0.1907 \\ 30 & 0.187 & 0.1873 \\ \hline \end{array}

Now what you have to do is first check whether this closeness in values continues for large $N$, and second simulate the estimation using the $c_4(N)$ correction factor, and compare it to yours. If these come out favorable, then you either a) have found a better, valid and useful (simpler to calculate) "rule of thumb"/substitute for the $c_4(N)$ correction factor, or b) you have found a better correction factor. If it is b), then it is publication material.

• Nice answer, though I'd encourage you to consider replacing the image with either a LaTeX table or a table built from code-formatting. – Glen_b Apr 14 '14 at 21:00
• Thanks @Glen_b but I think I do not know how to execute either... I made it a bit smaller so as it doesn't attack the viewer. – Alecos Papadopoulos Apr 14 '14 at 21:08
• That's an improvement, thanks. The code-formatting is just "indent text by 4 spaces to make it constant-width" by selecting a table of ascii text and pressing $\{\}$ at the top of the edit window. $\LaTeX$ support in MathJax doesn't do the tabular environment, but you can fake a table with the array environment. – Glen_b Apr 14 '14 at 21:30
• Like so. There are also some alternative approaches discussed here, e.g. the first table here. – Glen_b Apr 14 '14 at 21:39
• @Glen_b Latex shows in comments just fine (the "Show math as TeX Commands" option), and can be copy-pasted. – Alecos Papadopoulos Apr 14 '14 at 21:47

If I felt that the correction is necessary beyond the standard $n-1$ why would I do the rule of a thumb? I'd go for the exact expression if it is know, such as in the case of the normal distribution. I have never seen anyone using this rule of a thumb anyways.

So, my answer is no, you didn't find anything fantastic. At best you found marginally better fit to a practically useless rule of thumb applicable to normal distribution. This will certainly not work for all distributions. Check it yourself by replacing rnorm() in your code by something else, such as rchisq(size, df=0.1).

• a bit harsh perhaps? – user603 Apr 15 '14 at 9:36

For the sake of others who find this page, it is probably worth backing up a step and ask whether you really want an unbiased estimate of the population SD or an unbiased estimate of the population variance.

If you are going to use the SD to compute a confidence interval of a mean (or difference between two means) or to run a t test or ANOVA.., then I believe all the math is based on variances, not standard deviations. For these purposes, you want an unbiased variance, which is the standard deviation squared. If you compute the SD using the usual n-1 rule, the variance will be unbiased. But if you compute an unbiased SD, as you show here, then the variance would be biased.

• True. I was worried whether people might take it that literally as "uniformly better than Bessel". I've updated the conclusion to reflect that. – Jonas Lindeløv Apr 15 '14 at 23:22