To simplify, this means: if you have a following setup Cage 1: male1-control, male2-control, male3-treatment, male4-treatment Cage 2: female1-control, female2-

To simplify, this means: if you have a following setup Cage 1: male1-control, male2-control, male3-treatment, male4-treatment Cage 2: female1-control, female2-control, female3-treatment, female4-treatment and you see females responding to treatment better than males, what does that mean? It can be any of the following: 0: females respond differently to the treatment due to their biochemistry (say, some Y-chromosome gene is interfering with the treatment) 1: females respond differently to the treatment due to social effects (a male would respond to treatment but not in an environmnet where he has to compete with other males) 2: there is a cage effect (say, it is slighly warmer, or more light, or fresh breeze, or something else mice care about) that causes the animals in one cage to respond differently. Only with the above setup, you cannot know which answer is correct. Statisticians have a bunch of tricks to tackle some of the issues above. For example, you can crossover (swap cages after some time) or increase the number of cages and randomise across cage conditions (say, you have 20 cages, 10 all-male and 10 all-female, spread around the facility to cover the variability of the cage conditions). Still, you won't be able to distinguish between #0 and #1. This is not really my area though, so I want a knowledgeble reader to forgive my ignorance. I am a simple bioinformatician who was never too deeply involved into this type of pre-clinical biostatistics. I'm sure someone on HN has an answer how to properly design experiments like that. My message was that it's a bit more complicated than just 'we need female mice in research' - and I saw way too many irresposible biologists sacrificing 100s of mice in experiments where a correct inference couldn't be made.