1. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses
Statistics
Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

2. Where We’ve Been Calculated point estimators of population parameters
Used the sampling distribution of a statistic to assess the reliability of an estimate through a confidence interval
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

3. Where We’re Going Test a specific value of a population parameter
Measure the reliability of the test
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

4. 8.1: The Elements of a Test of Hypotheses
Confidence Interval
Where on the number line do the data point us?
(No prior idea about the value of the parameter.) µ? µ?
Hypothesis Test
Do the data point us to this particular value?
(We have a value in mind from the outset.)
µ0?
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

5. 8.1: The Elements of a Test of Hypotheses
Null Hypothesis: H0
This will be supported unless the data provide evidence that it is false
The status quo
Alternative Hypothesis: Ha
This will be supported if the data provide sufficient evidence that it is true
The research hypothesis
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

6. 8.1: The Elements of a Test of Hypotheses
If the test statistic has a high probability when H0 is true, then H0 is not rejected.
If the test statistic has a (very) low probability when H0 is true, then H0 is rejected.
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

7. 8.1: The Elements of a Test of Hypotheses
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

8. 8.1: The Elements of a Test of Hypotheses
Reality ↓ / Test Result →
Do not reject H0
Reject H0
H0 is true
Correct!
Type I Error: rejecting a true null hypothesis
P(Type I error) = 
H0 is false
Type II Error: not rejecting a false null hypothesis
P(Type II error) = 
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

9. 8.1: The Elements of a Test of Hypotheses
Note: Null hypotheses are either rejected, or else there is insufficient evidence to reject them. (I.e., we don’t accept null hypotheses.)
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

10. 8.1: The Elements of a Test of Hypotheses
Null hypothesis (H0): A theory about the values of one or more parameters
Ex.: H0: µ = µ0 (a specified value for µ)
Alternative hypothesis (Ha): Contradicts the null hypothesis
Ex.: H0: µ ≠ µ0
Test Statistic: The sample statistic to be used to test the hypothesis
Rejection region: The values for the test statistic which lead to rejection of the null hypothesis
Assumptions: Clear statements about any assumptions concerning the target population
Experiment and calculation of test statistic: The appropriate calculation for the test based on the sample data
Conclusion: Reject the null hypothesis (with possible Type I error) or do not reject it (with possible Type II error)
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

11. 8.1: The Elements of a Test of Hypotheses
Suppose a new interpretation of the rules by soccer referees is expected to increase the number of yellow cards per game. The average number of yellow cards per game had been 4. A sample of 121 matches produced an average of 4.7 yellow cards per game, with a standard deviation of .5 cards. At the 5% significance level, has there been a change in infractions called?
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

12. 8.1: The Elements of a Test of Hypotheses
Ha: µ ≠ 4
Sample statistic:  = 4.7
= .05
Assume the sampling distribution is normal.
Test statistic:
Conclusion: z.05 = Since z* > z.05 , reject H0.
(That is, there do seem to be more yellow cards.)
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

13. 8.2: Large-Sample Test of a Hypothesis about a Population Mean
Ha: µ < µ0
Ha: µ ≠ µ0
Ha: µ > µ0
The null hypothesis is
usually stated as an
equality …
… even though the alternative hypothesis can be either an equality or an inequality.
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

14. 8.2: Large-Sample Test of a Hypothesis about a Population Mean
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

15. 8.2: Large-Sample Test of a Hypothesis about a Population Mean
Rejection Regions for Common Values of 
Lower Tailed
Upper Tailed
Two tailed
 = .10
z <
z > 1.28
| z | > 1.645
 = .05
z <
z > 1.645
| z | > 1.96
 = .01
z <
z > 2.33
| z | > 2.575
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

16. 8.2: Large-Sample Test of a Hypothesis about a Population Mean
One-Tailed Test
Two-Tailed Test
H0 : µ = µ0
Ha : µ < or > µ0
Test Statistic:
Rejection Region: | z | > z
H0 : µ = µ0
Ha : µ ≠ µ0
Test Statistic:
Rejection Region: | z | > z/2
Conditions: 1) A random sample is selected from the target population.
2) The sample size n is large.
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

17. 8.2: Large-Sample Test of a Hypothesis about a Population Mean
The Economics of Education Review (Vol. 21, 2002) reported a mean salary for males with postgraduate degrees of $61,340, with an estimated standard error (s ) equal to $2,185. We wish to test, at the  = .05 level, H0: µ = $60,000.
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

18. 8.2: Large-Sample Test of a Hypothesis about a Population Mean
Ha : µ ≠ 60,000
Test Statistic:
Rejection Region: | z | > z.025 = 1.96
The Economics of Education Review (Vol. 21, 2002) reported a mean salary for males with postgraduate degrees of $61,340, with an estimated standard error (s) equal to $2,185. We wish to test, at the  = .05 level, H0: µ = $60,000.
Do not reject H0
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

19. 8.3:Observed Significance Levels: p - Values
Suppose z = P(z > 2.12) =
Reject H0 at the = .05 level
Do not reject H0 at the = .01 level
But it’s pretty close, isn’t it?
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

20. 8.3:Observed Significance Levels: p - Values
The observed significance level, or p-value, for a test is the probability of observing the results actually observed (z*) assuming the null hypothesis is true.
The lower this probability, the less likely H0 is true.
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

21. 8.3:Observed Significance Levels: p - Values
H0 : µ = 65,000
Ha : µ ≠ 65,000
Test Statistic:
p-value: P(  61,340 |H0 ) = P(|z| > 1.675) = .0475
Let’s go back to the Economics of Education Review report (= $61,340, s = $2,185). This time we’ll test H0: µ = $65,000.
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

22. 8.3:Observed Significance Levels: p - Values
Reporting test results
Choose the maximum tolerable value of 
If the p-value < , reject H0
If the p-value > , do not reject H0
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

23. 8.3:Observed Significance Levels: p - Values
Some stats packages will only report two-tailed p-values.
Converting a Two-Tailed p-Value to a One-Tailed p-Value
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

24. 8.3:Observed Significance Levels: p - Values
Some stats packages will only report two-tailed p-values.
Converting a Two-Tailed p-Value to a One-Tailed p-Value
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

25. 8.4: Small-Sample Test of a Hypothesis about a Population Mean
If the sample is small and  is unknown, testing hypotheses about µ requires the
t-distribution instead of the z-distribution.
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

26. 8.4: Small-Sample Test of a Hypothesis about a Population Mean
One-Tailed Test
Two-Tailed Test
H0 : µ = µ0
Ha : µ ≠ µ0
Test Statistic:
Rejection Region: | t | > t/2
H0 : µ = µ0
Ha : µ < or > µ0
Test Statistic:
Rejection Region: | t | > t
Conditions: 1) A random sample is selected from the target population.
2) The population from which the sample is selected is approximately normal.
3) The value of t is based on (n – 1) degrees of freedom
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

27. 8.4: Small-Sample Test of a Hypothesis about a Population Mean
Suppose copiers average 100,000 between paper jams. A salesman claims his are better, and offers to leave 5 units for testing. The average number of copies between jams is 100,987, with a standard deviation of Does his claim seem believable?
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

28. 8.4: Small-Sample Test of a Hypothesis about a Population Mean
Suppose copiers average 100,000 between paper jams. A salesman claims his are better, and offers to leave 5 units for testing. The average number of copies between jams is 100,987, with a standard deviation of Does his claim seem believable?
H0 : µ = 100,000
Ha : µ > 100,000
Test Statistic:
p-value: P(  100,987|H0 ) = P(|tdf=4| > 14.06) < .001
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

29. 8.4: Small-Sample Test of a Hypothesis about a Population Mean
Suppose copiers average 100,000 between paper jams. A salesman claims his are better, and offers to leave 5 units for testing. The average number of copies between jams is 100,987, with a standard deviation of Does his claim seem believable?
H0 : µ = 100,000
Ha : µ > 100,000
Test Statistic:
p-value: P(  100,987|H0 ) = P(|tdf=4| > 14.06) < .001
Reject the null hypothesis based on the very low probability of seeing the observed results if the null were true.
So, the claim does seem plausible.
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

30. 8.5: Large-Sample Test of a Hypothesis about a Population Proportion
One-Tailed Test
Two-Tailed Test
H0 : p = p0
Ha : p < or > p0
Test Statistic:
Rejection Region: | z | > z
H0 : p = p0
Ha : p ≠ p0
Test Statistic:
Rejection Region: | z | > z/2
p0 = hypothesized value of p, , and q0 = 1 - p0
Conditions: 1) A random sample is selected from a binomial population.
2) The sample size n is large (i.e., np0 and nq0 are both  15).
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

31. 8.5: Large-Sample Test of a Hypothesis about a Population Proportion
Rope designed for use in the theatre must withstand unusual stresses. Assume a brand of 3” three-strand rope is expected to have a breaking strength of 1400 lbs. A vendor receives a shipment of rope and needs to (destructively) test it.
The vendor will reject any shipment which cannot pass a 1% defect test (that’s harsh, but so is falling scenery during an aria) sections of rope are tested, with 20 pieces failing the test. At the  = .01 level, should the shipment be rejected?
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

32. 8.5: Large-Sample Test of a Hypothesis about a Population Proportion
H0: p = .01 Ha: p > .01 Rejection region: |z| > Test statistic:
The vendor will reject any shipment that cannot pass a 1% defects test sections of rope are tested, with 20 pieces failing the test. At the  = .01 level, should the shipment be rejected?
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

33. 8.5: Large-Sample Test of a Hypothesis about a Population Proportion
H0: p = .01 Ha: p > .01 Rejection region: |z| > Test statistic:
The vendor will reject any shipment that cannot pass a 1% defects test sections of rope are tested, with 20 pieces failing the test. At the  = .01 level, should the shipment be rejected?
There is insufficient evidence to reject the null hypothesis based on the sample results.
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

34. 8.6: Calculating Type II Error Probabilities: More about 
To calculate P(Type II), or , … 1. Calculate the value(s) of  that divide the “do not reject” region from the “reject” region(s). Upper-tailed test: Lower-tailed test: Two-tailed test:
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

35. 8.6: Calculating Type II Error Probabilities: More about 
To calculate P(Type II), or , … 1. Calculate the value(s) of  that divide the “do not reject” region from the “reject” region(s). 2. Calculate the z-value of 0 assuming the alternative hypothesis mean is the true µ: The probability of getting this z-value is .
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

36. 8.6: Calculating Type II Error Probabilities: More about 
The power of a test is the probability that the test will correctly lead to the rejection of the null hypothesis for a particular value of µ in the alternative hypothesis. The power of a test is calculated as (1 -  ).
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

37. 8.6: Calculating Type II Error Probabilities: More about 
The Economics of Education Review (Vol. 21, 2002) reported a mean salary for males with postgraduate degrees of $61,340, with an estimated standard error (s) equal to $2,185. We wish to test, at the  = .05 level, H0: µ = $60,000.
H0 : µ = 60,000
Ha : µ ≠ 60,000
Test Statistic: z = .613;
z=.025 = 1.96
We did not reject this null hypothesis earlier, but what if the true mean were $62,000?
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

38. 8.6: Calculating Type II Error Probabilities: More about 
The Economics of Education Review (Vol. 21, 2002) reported a mean salary for males with postgraduate degrees of $61,340, with s equal to $2,185. We did not reject this null hypothesis earlier, but what if the true mean were $62,000?
The power of this test is
= .6179
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

39. 8.6: Calculating Type II Error Probabilities: More about 
For fixed n and , the value of  decreases and the power increases as the distance between µ0 and µa increases.
For fixed n, µ0 and µa, the value of  increases and the power decreases as the value of  is decreased.
For fixed , µ0 and µa, the value of  decreases and the power increases as n is increased.
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

40. 8.6: Calculating Type II Error Probabilities: More about 
For fixed n and 
 decreases and (1-) increases
as |µ0 - µa| increases
For fixed n, µ0 and µa
 increases and (1 - ) decreases
as  decreases
 decreases and (1 -  ) increases
as n increases
For fixed , µ0 and µa
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

41. 8.7: Tests of Hypotheses about a Population Variance
Random Variable X
Sample Statistic:  or p-hat
Hypothesis Test on µ or p
Based on z
Based on t
Sample Statistic: s2
Hypothesis Test on 2 or 
Based on 2
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

42. 8.7: Tests of Hypotheses about a Population Variance
The chi-square distribution is really a family of distributions,
depending on the number of degrees of freedom.
But, the population must be normally distributed for the
hypothesis tests on 2 (or ) to be reliable!
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

43. 8.7: Tests of Hypotheses about a Population Variance
One-Tailed Test Test statistic: Rejection region:
Two-Tailed Test
Test statistic:
Rejection region:
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

44. 8.7: Tests of Hypotheses about a Population Variance
Conditions Required for a Valid
Large- Sample Hypothesis Test for 2
1. A random sample is selected from the target population.
2. The population from which the sample is selected is approximately normal.
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

45. 8.7: Tests of Hypotheses about a Population Variance
Earlier, we considered the average number of copies between jams for a brand of copiers. The salesman also claims his copiers are more predictable, in that the standard deviation of jams is In the sample of 5 copiers, that sample standard deviation was 157. Does his claim seem believable, at the  = .10 level?
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

46. 8.7: Tests of Hypotheses about a Population Variance
Earlier, we considered the average number of copies between jams for a brand of copiers. The salesman also claims his copiers are more predictable, in that the standard deviation of jams is In the sample of 5 copiers, that sample standard deviation was 157. Does his claim seem believable, at the  = .10 level?
Two-Tailed Test Test statistic: Rejection criterion:
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses

47. 8.7: Tests of Hypotheses about a Population Variance
Earlier, we considered the average number of copies between jams for a brand of copiers. The salesman also claims his copiers are more reliable, in that the standard deviation of jams is In the sample of 5 copiers, that sample standard deviation was 157. Does his claim seem believable, at the  = .10 level?
Two-Tailed Test Test statistic: Rejection criterion:
Do not reject the null hypothesis.
McClave, Statistics, 11th ed. Chapter 8: Inferences Based on a Single Sample: Tests of Hypotheses