Current clinical and biological studies apply different biotechnologies and subsequently combine the
resulting -omics data to test biological hypotheses. The plethora of -omics data and their combination generates
a large number of hypotheses and apparently increases the study power. In contrary to these expectations, the
wealth of -omics data may even reduce the statistical power of a study because of a large correction factor for
multiple testing. Typically this loss of power at -omics data is caused by an increased false detection rate (FDR)
in single measurements like falsely detected DNA copy numbers or falsely identified differentially expressed genes.
The false detections are likely to fail the test because they are random and, therefore, are not related to the
tested conditions. Thus, a high FDR at the detection level considerably decreases the discovery power of studies,
specifically if different -omics data are involved.