answer:
The central limit theorem is a mathematical principle that states that if you have a large enough sample size, the distribution of sample means will approach a normal distribution.
In other words, when you sample data from a population, the shape of the distribution of these sample means will become more and more normal as the sample size increases.
The effect of increasing the sample size on the sampling distribution is that it makes the distribution more and more normal. This is due to the Law of Large Numbers, which states that as the number of samples increases, the mean of the sample means will approach the true population mean.
When you increase the sample size, you are increasing the accuracy of your estimate of what the population means. This is because you are reducing the sampling error, which is the difference between the population mean and the sample mean. The sampling error decreases as the sample size increases.
In conclusion, increasing the sample size reduces sampling error and makes the sampling distribution more and more normal. This is because the Law of Large Numbers ensures that the sample means becoming more and more representative of the population mean as the sample size increases.