I have two bacterial srains - one is wild type and the other has one gene deleted. In my experiment, I calculate some phenotype for both of these strains. I repeat this experiment 5 times. Now I want to see if there is a difference in this phenotype between the two strains, so I perform a t-test (assuming the data is normally distributed for now). My question is - should I perform a paired t-test or unpaired? I am a bit confused about it since I feel I have some rationale for doing it either ways -

Why I think its **paired** : in every replicate, I compare the same two strains and I repeat this 5 times

Why I think its **unpaired** : Its not like I am subjecting the same strain to two different conditions (which often happens in paired tests). Rather, I have two different strains being subjected to the same condition.

Any help would be appreciated!

and

You deleted a gene and there is a phenotype that you are able to observe in all 5 replicates. In that case do you really need statistical evidence? Or is the phenotype subtle/variable so you can't say for sure that the gene deletion is causing that? If there is no visible phenotype then how are you quantifying the difference?

Thanks Genomax. The phenotype I measure is exhibited by all relevant bacterial strains, and I am trying to see if there is a significant difference in this phenotype, between the control and the deletion mutant. I'm sorry if I wasn't very clear earlier

A universal advice: use regression modelling instead of t-test =) I think your question will be answered right when you write down the necessary formula.

The answer for the simplest case will be equivalent to a t-test.

Hi, thanks for that comment. Since I comparing two samples, how do you suggest regression will help determine the difference? Because with a t-test it is quite straightforward

Simply code your groups as predictors and run a regression analysis =) but when you start to think about the design matrix - you immediately come to the answer which kind of model is the most suitable

https://lindeloev.github.io/tests-as-linear/

t-test does have a cool stuff such as non-homogenity of variances (Welch correction) but in practice it is not as suitable as possibility to model different confounders and interactions