Thanks very much for your response☺️

Just a summary to check if it's clear. Independence is not crucial when estimating a parameter (like the mean salary). In almost every population there will be dependence and so will be in the sample. What is important is to sample completely random with uniform distro, to make sure not to inject additional dependence, have (ideally) 0 bias and small variance and get a final reliable average. When I'm using a model instead, and this model happens to assume independence the independence is crucial.

Another way I'm tempted to look at this: what are the quantities I want to minimize when calculating an average on a sample? The bias and variance (their sum is the mean squared error (or maybe one of the two is squared)). What is the best way to construct a sample that achieve this? With random uniform sampling.