I am performing an experiment with bacteria that involves 2 different conditions and I am trying to see if there is a statistically significant difference between the two. Now I think there is a good amount of stochasticity in the experiment that can affect the results in a major way. So, I personally don't feel confident with just three replicates, since even one replicate with way-off outcomes (probably due to the stochasticity) can affect the conclusions a lot. So, I thought of doing 5 replicates for this study. I have not performed power analysis to come up with this number, but I just feel a bit more confident with n=5 than n=3 in this situation.

My question is, do I need to justify why I am working with 5 replicates rather than 3 (which seems to be the standard in biology)? Three replicates is the minimum number you need to calculate variance at all - I am not sure how one can be confident with just 3, especially when there is a lot of variability in the data. But still, just like 3 is an arbitrary number for replicates, would anyone object if it is 5?

Any suggestion/input would be appreciated.

PS - every replicate involves taking a sample from the same bacterial freezer stock, which was generated from a single colony of that bacteria. In other words, I don't think these are biological replicates but technical.

Thanks Lieven, it does seem now to me that these are biological replicates since I work with a different batch of bacteria in each replicate, even if they are all originally coming from the same colony.

Also, nice to know that you also feel that increasing the number of replicates is not something that needs formal justification and is just the right thing to do if possible. fortunately, in my case the experiments are pretty cheap to perform so I didn't have any problem with that.

I agree that more replicates is always better and I always welcome n=5 instead of n=3. However it is not true that n=3 is a technical minimum. It is perfectly possible to compute SDs from n=2 observations. The limma package for example is perfectly capable of undertaking a differential expression analysis even for n=1 in one group vs n=2 in another. That's not a recommendation, nor any encouragement to use such small samples, just a statement of what is mathematically possible!

Thanks for that, Gordon. I was thinking of ANOVA, but you are right, for variance calculation, it can technically be done with two samples too