When working with qPCR data, the general route to take is to obtain ddCT values by normalising to an internal reference gene followed by subtracting from the mean of the control values. This is followed by raising 2^-(ddCT) to obtain the final gene expression value.
We generally work with pooled samples of 2 experiments where each experiment consists of 4 biological replicates being tested for the control and treated condition.
Browsing the web and literature, I see no consensus on whether to use parametric tests or non parametric tests for testing for (the difference in whatever chosen statistic) significance. I see that online discussions in general state- if data is normally distributed across all treatments, then use a parametric test and vice versa.
But is this the right way to go about it? Especially given that our sample size is never greater than 8, should I at all be considering parametric tests?
I find that testing the data for normality yields contrasting results when I examine the same biological specimen (given the same treatments) in independently conducted experiments. Should I always err on the side of caution or use non-parametric tests?
For example, if I were to test for the population size of Drosophila in 10 vials, I would know that irrespective of whether I could actually obtain a normal distribution in my sampling, biological populations in general follow a normal distribution and therefore I would always apply a parametric test if comparing with another population!