Can somebody in basic terms-for I have never studied probability- explain what the "Strong Law of Large Numbers" as well as "Weak Law of Small Numbers".
The Law of Large Numbers says that the long-time sample mean of a series of independent observations approaches the theoretical mean of a single observation. That is, the sample mean ( a random quantity) becomes deterministic in the limit of large samples. It connects axiomatically based probability theory back to the frequentist definition of probability, an older approach.Originally Posted by ThePerfectHacker
Indirectly, yes. If your sample size is too large relative to the population, the members of the samples are "too dependent" (since you won't use the sample population member twice, your population changes as you are sampling). Then most of the usual theory becomes inapplicable. However, the LLN really only applied to cases where you can repeat a random experiment as many times as you want. So for a finite population, this works only if you are willing to sample the same member of the population more than once (sampling with replacement).Originally Posted by MathGuru