  The identification of increasingly smaller signal from objects observed with
a non-perfect instrument in a noisy environment poses a challenge for a
statistically clean data analysis. We want to compute the probability of
frequencies determined in various data sets to be related or not, which cannot
be answered with a simple comparison of amplitudes. Our method provides a
statistical estimator for a given signal with different strengths in a set of
observations to be of instrumental origin or to be intrinsic. Based on the
spectral significance as an unbiased statistical quantity in frequency
analysis, Discrete Fourier Transforms (DFTs) of target and background light
curves are comparatively examined. The individual False-Alarm Probabilities are
used to deduce conditional probabilities for a peak in a target spectrum to be
real in spite of a corresponding peak in the spectrum of a background or of
comparison stars. Alternatively, we can compute joint probabilities of
frequencies to occur in the DFT spectra of several data sets simultaneously but
with different amplitude, which leads to composed spectral significances. These
are useful to investigate a star observed in different filters or during
several observing runs. The composed spectral significance is a measure for the
probability that none of coinciding peaks in the DFT spectra under
consideration are due to noise. Cinderella is a mathematical approach to a
general statistical problem. Its potential reaches beyond photometry from
ground or space: to all cases where a quantitative statistical comparison of
periodicities in different data sets is desired. Examples for the composed and
the conditional Cinderella mode for different observation setups are presented.
