The Law of Large Numbers states that as the sample size increases, the sample mean will get closer to the population mean. This principle is fundamental in probability and statistics.
5 Must Know Facts For Your Next Test
The Law of Large Numbers applies only when samples are independent and identically distributed (i.i.d).
There are two forms: Weak Law of Large Numbers and Strong Law of Large Numbers.
This law helps justify the use of sample data to make inferences about a population.
In practical applications, larger sample sizes typically yield more accurate estimates.
The Central Limit Theorem relies on the principles outlined by the Law of Large Numbers.
States that, given a sufficiently large sample size from a population with a finite level of variance, the sample means will be approximately normally distributed.
Sample Mean: The average value calculated from a set of data points collected from a sample.