In CLT theoram: The bigger the samples, the closer to Normal ( n -> ∞ )
In the Pdf "Statistics - Course Notes. Page number: 21 titled "Central Limit Theorem"
The last point of the box titled "The theorem" says: The bigger the samples, the closer
to Normal ( n -> ∞ )
Got, it as with the bigger sample we would have more possibility of being accurate which means closer to the value of normal. But the notational representation in bracket ( n -> ∞ ) says that the sample size should tend to infinity. But I think it is incorrect as the population size is denoted as capital N. This means sample size lowercase n -> N for getting correct. How can any sample become greater than the size of the population itself?
Is my analogy correct?
Hello,
This is correct. If the sample size n tends to infinity then the distribution of the sample will approximate the Normal distribution.
Best,
Ned
I understood it as (n -> ∞) means as many samples as possible. assuming you have taken ∞ samples, then you could have taken everything in the population. so n -> ∞ is just same as n -> N
