Let's say I have two experiments of the same type, where I measure something x times in one location and y times in another location, is there any point in looking for a dependency in values between these two experiments? In case it matters, there's a difference in sample size and both samples have a near normal distribution.
For example, if I would want to measure the length of people in Asia and the length of people in Europe, I could investigate a null hypothesis test H0 'The average length of Asians is smaller than the average length of Europeans.'. This is a valid study.
However, it seems awkward to wonder if the length of Europeans could be influenced by the length of Asians. After all, the length can be influenced by other potential factors, like food, weather, genes, ... If the world population were suddenly to take in growth hormones, then the average length of both Asians and Europeans are likely to increase. It seems silly to assume Europeans increased in length because Asians did. Europeans did increase in size along with Asians, but not because of a direct result.
Is it only worth considering whether two variables have any sort of dependency if they have paired values (like age and weight)? If not, what would be the logical next step to take in order to test for dependency?
I've also noticed that I cannot perform any kind of correlation test since I do not have any paired values. Can I assume as an immediate result that my two variables have no linear dependency?
I tried to find the answer myself, but there are quite a lot of tests and things to be aware of; it gets a little overwhelming for a novice.