This document summarizes statistical hypothesis testing methods for continuous variables, including t-tests, F-tests, and z-tests. It provides examples of applying one-sample and two-sample t-tests to compare means from randomized experiments. It also discusses using F-tests to compare variances and z-tests for proportions from binomial and Poisson distributions with large samples. The document includes step-by-step solutions to examples testing differences in weights after treatment, red blood cell counts between males and females, biochemical indexes between patients and healthy people, positive testing rates between physicians and the general population, and measuring radioactivity levels.

1. Hypothesis Testing for Continuous Variables Yuantao Hao 19th,Otc., 2009 Chapter4

3. 4.3 The t Test for Data under Randomized Paired Design

6. Solution:

7. P ＞ 0.50

8. 4.4 The Tests for Comparing Two Means Based on Two Groups of Data under Completely Randomized Design

15. 4.4.1 Equal Variances

16. Solution: P ＜ 0.01

17. 4.4.2 Unequal Variances Satterthwaite’s method

19. Solution: P ＞ 0.05

20. 4.5 The F -Test for Equal Variances of Two Groups of Data under Completely Randomized Design

21. 4.5 The F -Test for Equal Variances of Two Groups

22.  1 =n 1 -1,  2 =n 2 -1

27. 4.6.1 The Z-test for the population probability of binomial distribution ( large n )

28. 4.6.1 The Z-test for the population probability n  5 n(1-  )  5

31. Solution:

35. Solution:

38. P =0.08

39. 4.6.2 The Z-test for the population mean of Poisson distribution ( largeλ )

40. 4.6.2 The Z-test for the population mean of Poisson distribution (largeλ)

42. Solution:

43. P = 0.13

45. Solution:

49. Solution: