STAB22H3 Lecture Notes - Lecture 10: Iif, Simple Random Sample, Type I And Type Ii Errors

• School: UTSC
• Department: Statistics
• Course: STAB22H3
• Professor: Mahinda Samarakoon

Document Summary

I statistics (sample count, sample mean x , sample proportion p) can change from one sample to another. I therefore they have a distribution, called the sampling distribution. I generate n = 10 values x1, x2, . I the sd of x n decreases as n increases. That is, x n estimates with greater precision when n is larger. Same experiment with non-normal underlying distribution of the population. Summary: suppose x has mean and sd . I with sd that decreases as n increases. That is, x n estimates with greater precision when n is large. Draw a srs of size n from a population with mean and sd . When n is large, the sampling distribution of a sample mean x is approximately normal with mean and sd / n. Note: the normal approximation for the sample proportion and counts (next slide) is an important example of the central limit theorem.