  Most physical data sets contain a stochastic contribution produced by
measurement noise or other random sources along with the signal. Usually,
neither the signal nor the noise are accurately known prior to the measurement
so that both have to be estimated a posteriori. We have studied a procedure to
estimate the standard deviation of the stochastic contribution assuming
normality and independence, requiring a sufficiently well-sampled data set to
yield reliable results. This procedure is based on estimating the standard
deviation in a sample of weighted sums of arbitrarily sampled data points and
is identical to the so-called DER_SNR algorithm for specific parameter
settings. To demonstrate the applicability of our procedure, we present
applications to synthetic data, high-resolution spectra, and a large sample of
space-based light curves and, finally, give guidelines to apply the procedure
in situation not explicitly considered here to promote its adoption in data
analysis.
