Stratified random sampling is a method of sampling that divides a population into smaller groups that form the basis of test samples.

Researchers argue that, in some situations where machine-learning models are used to allocate scarce resources or opportunities, randomizing decisions in a structured way may lead to fairer outcomes.

Create an rng object with np.random.default_rng(), you can seed it for reproducible results. You can draw samples from probability distributions, including from the binomial and normal distributions.