  We discuss methods currently in use for determining the significance of peaks
in the periodograms of time series. We discuss some general methods for
constructing significance tests, false alarm probability functions, and the
role played in these by independent random variables and by empirical and
theoretical cumulative distribution functions. We also discuss the concept of
"independent frequencies" in periodogram analysis. We propose a practical
method for estimating the significance of periodogram peaks, applicable to all
time series irrespective of the spacing of the data. This method, based on
Monte Carlo simulations, produces significance tests that are tailor-made for
any given astronomical time series.
