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and technical replicates. The quality of an experiment design is commonly assessed by calculating its pro-tions. The ability of the researcher to obtain a statistically significant result from the testing of these hypotheses is governed by three factors: (1) the ‘treatment effect’, that is the magnitude of the mean dif-ferential expression between the chosen subpopulations; The concept of treatment effect and measurement variability is the basis of statistical power. The power of a statistical test is the probability of rejecting the null hypothesis, given that the null is false and the alternative hypothesis is true [7]. In other words, the power is the biological resolution of the experiment; it quanti-fies the likelihood of being able to resolve any differential expres-sion between treatment groups based on the variance of available measurements. Power increases with increasing magnitude of the differential expression, increasing number of biological replicates, increasing measurement precision, and decreasing biological vari-ability. The objective is to maximise the statistical resolution of th