So how can we say anything about the distribution of 100-people-averages — called the sampling distribution — when we don't know anything about the distribution of weight across the population? This is where the central limit theorem comes in: it says that for a big enough sample your sampling distribution is approximated by anormal distribution — this is the distribution with the famous bell shape. (A convention is that a sample size of 30 is good enough.)