Standard Deviation Annualized
It depends on the frequency of the data points. In general, if there are $T$ data points per year (and subject to some conditions*) then the conversion formula is
$$\sigma_{\rm annual} = \sigma_{\rm measured} \sqrt{T}$$
For example, in finance it is common to measure the return on a stock every day, but to quote volatility (aka standard deviation of returns) as an annual figure. There are about 260 trading days in a year, so you commonly see
$$\sigma_{\rm annual} = \sigma_{\rm daily} \times \sqrt{260}$$
[*] The conditions are as follows:
The annual quantity can be expressed as a sum of the quantities measured on a smaller timescale, that is, $$X_{\rm Year\,1} = x_1 + x_2 + \cdots + x_T$$
There is no autocorrelation among the quantities on the smaller timescale.
The the square root is most easily explained by noting that, subject to the conditions above, the variance increases in proportion to the elapsed time, and the variance is the square of the standard deviation. This is not too difficult to prove by starting from the definition of the annualized variance in terms of the micro-quantities $x_t$.