  You measure the value of a quantity x for a number of systems (cells,
molecules, people, chunks of metal, DNA vectors, etc.). You repeat the whole
set of measures in different occasions or assays, which you try to design as
equal to one another as possible. Despite the effort, you find that the results
are too different from one assay to another. As a consequence, some systems'
averages present standard deviations that are too large to render the results
statistically significant. In this work, we present a novel correction method
of very low mathematical and numerical complexity that can reduce the standard
deviation in your results and increase their statistical significance as long
as two conditions are met: inter-system variations of x matter to you but its
absolute value does not, and the different assays display a similar tendency in
the values of x; in other words, the results corresponding to different assays
present high linear correlation. We demonstrate the improvement that this
method brings about on a real cell biology experiment, but the method can be
applied to any problem that conforms to the described structure and
requirements, in any quantitative scientific field that has to deal with data
subject to uncertainty.
