STAT 301 Lecture Notes - Lecture 12: Central Limit Theorem, Statistical Inference, Sampling Distribution
Document Summary
Two results that are important in establishing the basis for inferential statistics are: Tells us what tends to happen to a sample mean as the sample size gets bigger. As our sample size increases, the average of our sample will tend to get closer and closer to the true average of the population from which we are sampling. If you flip a coin twice, you may get two heads. You will have flipped heads on average 100% of the time. In this case you will have flipped heads on average 0% of the time. With the law of large number, if you flip a coin 1000000 times, you are unlikely to flip heads exactly 50% of the time. The larger the sample size, the more likely your average will be accurate. Tells us that any distribution (no matter how skewed or strange) will produce a normal distribution of sample means if you take large enough samples from it.
