Figure 2. Factors that influence the statistical power in multi-omics studies.

The statistical power of a multi-omics study can be effected by several factors including and must be considered at the beginning of the study. Such factors include, but are not limited to (the effect of the following factors on power are under the assumption that the remaining factors remain constant): (1) The type of the study. While randomized controlled studies are generally more powerful than observational studies due to controlling unwanted effects, limitations can prohibit this application of a randomized controlled study. (2) The sample allocation. In general, a balanced study, where samples are equally distributed among group, is more powerful unbalanced study. (3) Sample size. As the number of samples in a study increases the statistical power improves. (4) Effect size. The greater the true differences between groups, the greater the statistical power of a study. (5) Hypothesis test. While parametric tests are in general more powerful than nonparametric test, parametric tests are not applicable if there assumptions are not met. (6) Significance level α. The significant level represents the probability of type I errors, the probability of rejecting the null hypothesis given that the null hypothesis is true. As the numerical value of α increases, the probability of type I errors increases as well as the statistical power (probability of rejecting the null hypothesis given that the null hypothesis is true). (7) Number of tests. Testing multiple hypotheses requires a correction and reduces the statistical power. (8) Background noise and sample variation increase the variance and complicate the detection of a true signal and therefore decrease the statistical power. (9) Confounders can increase variance and/or introduce a bias, which decreases the statistical power.