> Bit something I learned in my college statistics courses is that with a large enough sample, self-selection bias stops becoming a problem. What you might hav

> Bit something I learned in my college statistics courses is that with a large enough sample, self-selection bias stops becoming a problem. What you might have learned is that small (random) samples might not represent the full population, but as you get bigger (random) samples they tend to get closer to the true values. However, if you sample badly, errors will persist even when you sample more. Example - estimating height in a population: - If I get a perfect random sample of people then I can estimate population height really well, even with a smallish sample. The estimate gets better the more people I (randomly) sample. - However if there's selection bias in my sampling and I only sample women, then no matter how many women I sample I'm going to be getting a bad estimate of height across the full population, because I'm excluding men who are taller. Sample size can't overcome selection bias, you need to use other techniques to manage it.