The document discusses hypothesis testing and type 1 and type 2 errors. It provides an example of hypothesis testing comparing the proportion of defective items from two populations. The null hypothesis is that the true proportions are equal, and the alternative hypothesis is that they are not equal. It shows the calculation of the test statistic and comparison to the critical value to determine whether to reject the null hypothesis.

5. 1
HO: µ=µHO=100
Ha: µ ≠ µHO

6. 2

7. 3
True state of null hypothesis
Your Statistical
Decision H0 True H0 False
Reject H0 Type I error (α) Correct
Do not reject H0 Correct Type II Error (β)

8. 4

11. A

12. 1
2

13. 3
4

14. B

15. 1
2

16. 3
4
5

17. A
P=(n1p1+n2p2)/(n1+n2)

19. P=(n1p1+n2p2)/(n1+n2)
=(200(10/200)+100(4/100))/(200+100)
=0.046
1-P=q; 1-0.046=0.954
S.E=√pq(1/n1+1/n2)
=√0.046×0.954(1/200+1/100) =0.025
Z=(p1-p2)/ S.E
=(0.o5-0.04)/0.025 = 0.4
Standard Value=1.96

20. B