How do I combine standard deviations of two groups? I have 2 groups of people. I'm working with the data about their age. I know the means, the standard deviations and the number of people. I don't know the data of each person in the groups.
Group 1 :
Mean = 35 years old; SD = 14; n = 137 people
Group 2 :
Mean = 31 years old; SD = 11; n = 112 people
I want to combine those 2 groups to obtain a new mean and SD. It's easy for the mean, but is it possible for the SD? I do not know the distribution of those samples, and I can't assume those are normal distributions. Is there a formula for distributions that aren't necessarily normal?
 A: Continuing on from BruceET's explanation, note that if we are computing the unbiased estimator of the standard deviation of each sample, namely $$s = \sqrt{\frac{1}{n-1} \sum_{i=1}^n (x_i - \bar x)^2},$$ and this is what is provided, then note that for samples $\boldsymbol x = (x_1, \ldots, x_n)$, $\boldsymbol y = (y_1, \ldots, y_m)$, let $\boldsymbol z = (x_1, \ldots, x_n, y_1, \ldots, y_m)$ be the combined sample, hence the combined sample mean is $$\bar z  = \frac{1}{n+m} \left( \sum_{i=1}^n x_i + \sum_{j=1}^m y_i \right) = \frac{n \bar x + m \bar y}{n+m}.$$  Consequently, the combined sample variance is $$s_z^2 = \frac{1}{n+m-1} \left( \sum_{i=1}^n (x_i - \bar z)^2 + \sum_{j=1}^m (y_i - \bar z)^2 \right),$$ where it is important to note that the combined mean is used.  In order to have any hope of expressing this in terms of $s_x^2$ and $s_y^2$, we clearly need to decompose the sums of squares; for instance, $$(x_i - \bar z)^2 = (x_i - \bar x + \bar x - \bar z)^2 = (x_i - \bar x)^2 + 2(x_i - \bar x)(\bar x - \bar z) + (\bar x - \bar z)^2,$$ thus $$\sum_{i=1}^n (x_i - \bar z)^2 = (n-1)s_x^2 + 2(\bar x - \bar z)\sum_{i=1}^n (x_i - \bar x) + n(\bar x - \bar z)^2.$$  But the middle term vanishes, so this gives $$s_z^2 = \frac{(n-1)s_x^2 + n(\bar x - \bar z)^2 + (m-1)s_y^2 + m(\bar y - \bar z)^2}{n+m-1}.$$  Upon simplification, we find $$n(\bar x - \bar z)^2 + m(\bar y - \bar z)^2 = \frac{mn(\bar x - \bar y)^2}{m + n},$$ so the formula becomes $$s_z^2 = \frac{(n-1) s_x^2 + (m-1) s_y^2}{n+m-1} + \frac{nm(\bar x - \bar y)^2}{(n+m)(n+m-1)}.$$  This second term is the required correction factor.
