A common method used in High Energy Physics to perform measurements
is the maximum Likelihood method, exploiting discriminating variables
to disentangle signal from background. The crucial point for such an
analysis to be reliable is to use an exhaustive list of sources of
events combined with an accurate description of all the Probability
Density Functions (PDF).

To assess the validity of the fit, a convincing quality check is to
explore further the data sample by examining the distributions of
control variables. A control variable can be obtained for instance by
removing one of the discriminating variables before performing again
the maximum Likelihood fit: this removed variable is a control
variable. The expected distribution of this control variable, for
signal, is to be compared to the one extracted, for signal, from the
data sample. In order to be able to do so, one must be able to unfold
from the distribution of the whole data sample.

The SPlot method allows to reconstruct the distributions for the
control variable, independently for each of the various sources of
events, without making use of any a priori knowledge on this
variable. The aim is thus to use the knowledge available for the
discriminating variables to infer the behavior of the individual
sources of events with respect to the control variable.

SPlot is optimal if the control variable is uncorrelated with the
discriminating variables.