The correct answer is: B. LLN
The Law of Large Numbers (LLN) states that the arithmetic mean of a sufficiently large number of iterates of independent random variables, each with a well-defined expected value, will be arbitrarily close to that expected value with high probability.
In other words, the LLN states that the sample mean will converge to the population mean as the sample size increases.
The LLN is a fundamental result in probability theory and statistics, and it has a wide range of applications. For example, it can be used to justify the use of confidence intervals and hypothesis tests.
The LLN can be proved in a number of ways. One common approach is to use the central limit theorem. The central limit theorem states that, as the sample size increases, the sampling distribution of the sample mean will approach a normal distribution with mean equal to the population mean and variance equal to the population variance divided by the sample size.
Another approach to proving the LLN is to use the strong law of large numbers. The strong law of large numbers states that, as the sample size increases, the sample mean will converge to the population mean almost surely.
The LLN is a powerful tool that can be used to make inferences about populations from samples. It is a fundamental result in probability theory and statistics, and it has a wide range of applications.