Lecture slides stats1.13.l09.air
- 1. Statistics One Lecture 9 The Central Limit Theorem 1
- 2. Two segments • Sampling distributions • Central limit theorem 2
- 3. Lecture 9 ~ Segment 1 Sampling distributions 3
- 4. Review of histograms • Histograms are used to display distributions • For example, the body temperature of a random sample of healthy people 4
- 8. Review of histograms • If a distribution is perfectly normal then the properties of the distribution are known 8
- 10. The normal distribution & probability • This allows for predictions about the distribution – Predictions aren’t certain – They are probabilistic 10
- 11. The normal distribution & probability • If one person is randomly selected from the sample, what is the probability that his or her body temperature is less than Z = 0? – Easy, p = .50 11
- 12. The normal distribution & probability • If one person is randomly selected from the sample, what is the probability that his or her body temperature is greater than Z = 2? (100 F°, 38 C°)? – p = .02 12
- 13. The normal distribution & probability • If this sample is healthy, then no one should have a fever • I detected a person with a fever • Therefore, this sample is not 100% healthy 13
- 14. Sampling distribution • A distribution of sample statistics, obtained from multiple samples – For example, • Distribution of sample means • Distribution of sample correlations • Distribution of sample regression coefficients 14
- 15. Sampling distribution • It is hypothetical – Assume a mean is calculated from a sample, obtained randomly from the population – Assume a certain sample size, N – Now, assume we had multiple random samples, all of size N, and therefore many sample means – Collectively, they form a sampling distribution 15
- 16. Sampling distribution & probability • If one sample is obtained from a normal healthy population, what is the probability that the sample mean is less than Z = 0? – Easy, p = .50 16
- 17. Sampling distribution & probability • If one sample is obtained from a normal healthy population, what is the probability that the sample mean is greater than Z = 2 (100 F°, 38 C°)? – p = .02 17
- 18. Sampling distribution & probability • If this population is healthy, then no one sample should have a high mean body temperature • I obtained a very high sample mean • Therefore, the population is not healthy 18
- 19. Sampling distribution • A distribution of sample statistics, obtained from multiple samples, each of size N – Distribution of sample means – Distribution of sample correlations – Distribution of sample regression coefficients 19
- 20. END SEGMENT 20
- 21. Lecture 9 ~ Segment 2 The Central Limit Theorem 21
- 22. Central Limit Theorem • Three principles – The mean of a sampling distribution is the same as the mean of the population – The standard deviation of the sampling distribution is the square root of the variance of sampling distribution σ2 = σ2 /N – The shape of a sampling distribution is approximately normal if either (a) N >= 30 or (b) the shape of the population distribution is normal 22
- 23. NHST & Central limit theorem • Multiple regression – – – – Assume the null hypothesis is true Conduct a study Calculate B, SE, and t t = B/SE 23
- 24. NHST & Central limit theorem • Multiple regression – If the null hypothesis is true (B=0), then no one sample should have a very low or very high B – I obtained a very high B – Therefore, Reject the null hypothesis 24
- 26. The family of t distributions 26
- 27. NHST & Central limit theorem • Multiple regression – – – – – Assume the null hypothesis is true Conduct a study Calculate B, SE, and t t = B/SE p-value is a function of t and sample size 27
- 28. NHST & the central limit theorem • Multiple regression – If the null hypothesis is true (B=0), then no one sample should have a very low or very high B – I obtained a very high B – Therefore, Reject the null hypothesis – Very high and very low is p < .05 28
- 29. NHST & the central limit theorem • Remember that sampling error, and therefore standard error, is largely determined by sample size 29
- 30. Sampling error and sample size 30
- 31. Sampling error and sample size 31
- 32. Central Limit Theorem • Three principles – The mean of a sampling distribution is the same as the mean of the population – The standard deviation of the sampling distribution is the square root of the variance of sampling distribution σ2 = σ2 /N – The shape of a sampling distribution is approximately normal if either (a) N >= 30 or (b) the shape of the population distribution is normal 32
- 33. END SEGMENT 33
- 34. END LECTURE 9 34