Chapter 9 of the document discusses the fundamentals of hypothesis testing, covering key concepts like null and alternative hypotheses, types of errors, and testing methodologies including z and t tests. It emphasizes the significance of distinguishing between type I and type II errors and provides frameworks for determining critical values and p-values for hypothesis tests. The chapter further connects hypothesis testing to confidence interval estimation, illustrating these concepts with examples.

1. © 2003 Prentice-Hall, Inc. Chap 9-1
Basic Business Statistics
(9th
Edition)
Chapter 9
Fundamentals of Hypothesis
Testing: One-Sample Tests

2. © 2003 Prentice-Hall, Inc.
Chap 9-2
Chapter Topics
 Hypothesis Testing Methodology
 Z Test for the Mean ( Known)
 p-Value Approach to Hypothesis Testing
 Connection to Confidence Interval Estimation
 One-Tail Tests
 t Test for the Mean ( Unknown)
 Z Test for the Proportion
 Potential Hypothesis-Testing Pitfalls and
Ethical Issues
σ
σ

3. © 2003 Prentice-Hall, Inc.
Chap 9-3
What is a Hypothesis?
 A Hypothesis is a
Claim (Assumption)
about the Population
Parameter
 Examples of parameters
are population mean
or proportion
 The parameter must
be identified before
analysis
I claim the mean GPA of
this class is 3.5!
© 1984-1994 T/Maker Co.
µ =

4. © 2003 Prentice-Hall, Inc.
Chap 9-4
The Null Hypothesis, H0
 States the Assumption (Numerical) to be
Tested
 E.g., The mean GPA is 3.5

 Null Hypothesis is Always about a Population
Parameter ( ), Not about a Sample
Statistic ( )
 Is the Hypothesis a Researcher Tries to
Reject
0 : 3.5H µ =
0 : 3.5H µ =
0 : 3.5H X =

5. © 2003 Prentice-Hall, Inc.
Chap 9-5
The Null Hypothesis, H0
 Begin with the Assumption that the Null
Hypothesis is True
 Similar to the notion of innocent until
proven guilty
 Refer to the Status Quo
 Always Contains the “=” Sign
 The Null Hypothesis May or May Not be
Rejected
(continued)

6. © 2003 Prentice-Hall, Inc.
Chap 9-6
The Alternative Hypothesis, H1
 Is the Opposite of the Null Hypothesis
 E.g., The mean GPA is NOT 3.5 ( )
 Challenges the Status Quo
 Never Contains the “=” Sign
 The Alternative Hypothesis May or May Not
Be Accepted (i.e., The Null Hypothesis May
or May Not Be Rejected)
 Is Generally the Hypothesis that the
Researcher Claims
1 : 3.5H µ ≠

7. © 2003 Prentice-Hall, Inc.
Chap 9-7
Error in Making Decisions
 Type I Error
 Reject a true null hypothesis

When the null hypothesis is rejected, we can
say that “We have shown the null hypothesis
to be false (with some ‘slight’ probability, i.e.
, of making a wrong decision)
 Has serious consequences
 Probability of Type I Error is

Called level of significance

Set by researcher
α
α

8. © 2003 Prentice-Hall, Inc.
Chap 9-8
Error in Making Decisions
 Type II Error
 Fail to reject a false null hypothesis
 Probability of Type II Error is
 The power of the test is
 Probability of Not Making Type I Error

 Called the Confidence Coefficient
( )1 α−
(continued)
( )1 β−
β

9. © 2003 Prentice-Hall, Inc.
Chap 9-9
Hypothesis Testing Process
Identify the Population
Assume the
population
mean GPA is 3.5
( )
REJECT
Take a Sample
Null Hypothesis
No, not likely!
X 2.4 likely ifIs 3.5?µ= =
0 : 3.5H µ =
( )2.4X =

10. © 2003 Prentice-Hall, Inc.
Chap 9-10
= 3.5
It is unlikely that
we would get a
sample mean of
this value ...
... if in fact this were
the population mean.
... Therefore,
we reject the
null hypothesis
that = 3.5.
Reason for Rejecting H0
µ
Sampling Distribution of
2.4
If H0 is true
X
X
µ

11. © 2003 Prentice-Hall, Inc.
Chap 9-11
Level of Significance,
 Defines Unlikely Values of Sample Statistic if
Null Hypothesis is True
 Called rejection region of the sampling distribution
 Designated by , (level of significance)
 Typical values are .01, .05, .10
 Selected by the Researcher at the Beginning
 Controls the Probability of Committing a Type
I Error
 Provides the Critical Value(s) of the Test
α
α

12. © 2003 Prentice-Hall, Inc.
Chap 9-12
Level of Significance and the
Rejection Region
H0: µ ≥ 3.5
H1: µ < 3.5
0
0
0
H0: µ ≤ 3.5
H1: µ > 3.5
H0: µ = 3.5
H1: µ ≠
3.5
α
α
α/2
Critical
Value(s)
Rejection
Regions

13. © 2003 Prentice-Hall, Inc.
Chap 9-13
Result Probabilities
H0: Innocent
The Truth The Truth
Verdict Innocent Guilty Decision H0 True H0 False
Innocent Correct Error
Do Not
Reject
H0
1 - α
Type II
Error (β )
Guilty Error Correct Reject
H0
Type I
Error
(α )
Power
(1 - β )
Jury Trial Hypothesis Test

14. © 2003 Prentice-Hall, Inc.
Chap 9-14
Type I & II Errors Have an
Inverse Relationship
α
β
Reduce probability of one error
and the other one goes up holding
everything else unchanged.

15. © 2003 Prentice-Hall, Inc.
Chap 9-15
Factors Affecting Type II Error
 True Value of Population Parameter
 increases when the difference between the
hypothesized parameter and its true value
decrease
 Significance Level
 increases when decreases
 Population Standard Deviation
 increases when increases
 Sample Size
 increases when n decreases
β
β
α
β σ
β n
α
β
β
β σ

16. © 2003 Prentice-Hall, Inc.
Chap 9-16
How to Choose between Type I
and Type II Errors
 Choice Depends on the Cost of the Errors
 Choose Smaller Type I Error When the Cost
of Rejecting the Maintained Hypothesis is
High
 A criminal trial: convicting an innocent person
 The Exxon Valdez: causing an oil tanker to sink
 Choose Larger Type I Error When You Have
an Interest in Changing the Status Quo
 A decision in a startup company about a new piece
of software
 A decision about unequal pay for a covered group

17. © 2003 Prentice-Hall, Inc.
Chap 9-17
Critical Values Approach to
Testing
 Convert Sample Statistic (e.g., ) to
Test Statistic (e.g., Z, t or F –statistic)
 Obtain Critical Value(s) for a Specified
from a Table or Computer
 If the test statistic falls in the critical region,
reject H0
 Otherwise, do not reject H0
X
α

18. © 2003 Prentice-Hall, Inc.
Chap 9-18
p-Value Approach to Testing
 Convert Sample Statistic (e.g., ) to Test
Statistic (e.g., Z, t or F –statistic)
 Obtain the p-value from a table or computer
 p-value: probability of obtaining a test statistic as
extreme or more extreme ( or ) than the
observed sample value given H0 is true
 Called observed level of significance
 Smallest value of that an H0 can be rejected
 Compare the p-value with
 If p-value , do not reject H0
 If p-value , reject H0
X
≤ ≥
<
≥ α
α
α
α

19. © 2003 Prentice-Hall, Inc.
Chap 9-19
General Steps in Hypothesis
Testing
E.g., Test the Assumption that the True Mean # of
TV Sets in U.S. Homes is at Least 3 ( Known)σ
1. State the H0
2. State the H1
3. Choose
4. Choose n
5. Choose Test
0
1
: 3
: 3
=.05
100
Z
H
H
n
test
µ
µ
α
≥
<
=
α

20. © 2003 Prentice-Hall, Inc.
Chap 9-20
100 households surveyed
Computed test stat =-2,
p-value = .0228
Reject null hypothesis
The true mean # TV set is
less than 3
(continued)
Reject H0
α
-1.645
Z
6. Set up critical value(s)
7. Collect data
8. Compute test statistic
and p-value
9. Make statistical
decision
10.Express conclusion
General Steps in Hypothesis
Testing

21. © 2003 Prentice-Hall, Inc.
Chap 9-21
One-Tail Z Test for Mean
( Known)
 Assumptions
 Population is normally distributed
 If not normal, requires large samples
 Null hypothesis has or sign only
 is known
 Z Test Statistic

σ
≤ ≥
/
X
X
X X
Z
n
µ µ
σ σ
− −
= =
σ

22. © 2003 Prentice-Hall, Inc.
Chap 9-22
Rejection Region
Z0
Reject H0
Z0
Reject H0
H0: µ ≥ µ0
H1: µ < µ0
H0: µ ≤ µ0
H1: µ > µ0
Z must be significantly
below 0 to reject H0
Small values of Z don’t
contradict H0 ; don’t reject
H0 !
αα

23. © 2003 Prentice-Hall, Inc.
Chap 9-23
Example: One-Tail Test
Does an average box of
cereal contain more than
368 grams of cereal? A
random sample of 25 boxes
showed = 372.5. The
company has specified σ to
be 15 grams. Test at the
α = 0.05 level.
368 gm.
H0:
µ ≤ 368
H1: µ > 368
X

24. © 2003 Prentice-Hall, Inc.
Chap 9-24
Reject and Do Not Reject
Regions
Z0 1.645
.05
Reject
1.50
X
368XXµ µ= = 372.5
0 : 368H µ ≤
1 : 368H µ >
Do Not Reject

25. © 2003 Prentice-Hall, Inc.
Chap 9-25
Finding Critical Value: One-Tail
Z .04 .06
1.6 .9495 .9505 .9515
1.7 .9591 .9599 .9608
1.8 .9671 .9678 .9686
.9738 .9750
Z0 1.645
.05
1.9 .9744
Standardized Cumulative
Normal Distribution Table
(Portion)
What is Z given α = 0.05?
α = .05
Critical Value
= 1.645
.95
1Zσ =

26. © 2003 Prentice-Hall, Inc.
Chap 9-26
Example Solution: One-Tail Test
α = 0.5
n = 25
Critical Value: 1.645
Conclusion:
Do Not Reject at α = .05.
Insufficient Evidence that
True Mean is More Than 368.
Z0 1.645
.05
Reject
H0: µ ≤ 368
H1: µ > 368
1.50
X
Z
n
µ
σ
−
= =
1.50

27. © 2003 Prentice-Hall, Inc.
Chap 9-27
p -Value Solution
Z0 1.50
p-Value =.0668
Z Value of Sample
Statistic
From Z Table:
Lookup 1.50 to
Obtain .9332
Use the
alternative
hypothesis
to find the
direction of
the rejection
region.
1.0000
- .9332
.0668
p-Value is P(Z ≥ 1.50) = 0.0668

28. © 2003 Prentice-Hall, Inc.
Chap 9-28
p -Value Solution (continued)
0
1.50
Z
Reject
(p-Value = 0.0668) ≥ (α = 0.05)
Do Not Reject.
p Value = 0.0668
α = 0.05
Test Statistic 1.50 is in the Do Not Reject
Region
1.645

29. © 2003 Prentice-Hall, Inc.
Chap 9-29
One-Tail Z Test for Mean
( Known) in PHStat
 PHStat | One-Sample Tests | Z Test for the
Mean, Sigma Known …
 Example in Excel Spreadsheet
σ

30. © 2003 Prentice-Hall, Inc.
Chap 9-30
Example: Two-Tail Test
Does an average box of
cereal contain 368 grams of
cereal? A random sample
of 25 boxes showed =
372.5. The company has
specified σ to be 15 grams
and the distribution to be
normal. Test at the
α = 0.05 level.
368 gm.
H0: µ = 368
H1: µ ≠ 368
X

31. © 2003 Prentice-Hall, Inc.
Chap 9-31
Reject and Do Not Reject
Regions
Z0 1.96
.025
Reject
-1.96
.025
1.50
X
368XXµ µ= = 372.5
Reject
0 : 368H µ =
1 : 368H µ ≠

32. © 2003 Prentice-Hall, Inc.
Chap 9-32
372.5 368
1.50
15
25
X
Z
n
µ
σ
− −
= = =α = 0.05
n = 25
Critical Value: ±1.96
Example Solution: Two-Tail Test
Test Statistic:
Decision:
Conclusion:
Do Not Reject at α = .05.
Z0 1.96
.025
Reject
-1.96
.025
H0: µ = 368
H1: µ ≠ 368
1.50
Insufficient Evidence that
True Mean is Not 368.

33. © 2003 Prentice-Hall, Inc.
Chap 9-33
p-Value Solution
(p-Value = 0.1336) ≥ (α = 0.05)
Do Not Reject.
0 1.50
Z
Reject
α = 0.05
1.96
p-Value = 2 x 0.0668
Test Statistic 1.50 is in the Do Not Reject
Region
Reject

34. © 2003 Prentice-Hall, Inc.
Chap 9-34
 PHStat | One-Sample Tests | Z Test for the
Mean, Sigma Known …
 Example in Excel Spreadsheet
Two-Tail Z Test for Mean
( Known) in PHStatσ

35. © 2003 Prentice-Hall, Inc.
Chap 9-35
( ) ( )
For 372.5, 15 and 25,
the 95% confidence interval is:
372.5 1.96 15/ 25 372.5 1.96 15/ 25
or
366.62 378.38
X nσ
µ
µ
= = =
− ≤ ≤ +
≤ ≤
Connection to Confidence
Intervals
We are 95% confident that the population mean is
between 366.62 and 378.38.
If this interval contains the hypothesized mean (368),
we do not reject the null hypothesis.
It does. Do not reject.

36. © 2003 Prentice-Hall, Inc.
Chap 9-36
t Test: Unknown
 Assumption
 Population is normally distributed
 If not normal, requires a large sample
 is unknown
 t Test Statistic with n-1 Degrees of Freedom

σ
/
X
t
S n
µ−
=
σ

37. © 2003 Prentice-Hall, Inc.
Chap 9-37
Example: One-Tail t Test
Does an average box of
cereal contain more than
368 grams of cereal? A
random sample of 36
boxes showed X = 372.5,
and s = 15. Test at the
α = 0.01 level.
368 gm.
H0: µ ≤ 368
H1: µ >
368
σ is not given

38. © 2003 Prentice-Hall, Inc.
Chap 9-38
Reject and Do Not Reject
Regions
t35
0 2.4377
.01
Reject
1.80
X
368XXµ µ= = 372.5
0 : 368H µ ≤
1 : 368H µ >
Do Not Reject

39. © 2003 Prentice-Hall, Inc.
Chap 9-39
Example Solution: One-Tail
α = 0.01
n = 36, df = 35
Critical Value: 2.4377
Test Statistic:
Decision:
Conclusion:
Do Not Reject at a = .01.
t35
0 2.4377
.01
Reject
H0: µ ≤ 368
H1: µ > 368
372.5 368
1.80
15
36
X
t
S
n
µ− −
= = =
1.80
Insufficient Evidence that
True Mean is More Than 368.

40. © 2003 Prentice-Hall, Inc.
Chap 9-40
p -Value Solution
0 1.80
t35
Reject
(p-Value is between .025 and .05) ≥ (α = 0.01)
Do Not Reject.
p-Value = [.025, .05]
α = 0.01
Test Statistic 1.80 is in the Do Not Reject
Region
2.4377

41. © 2003 Prentice-Hall, Inc.
Chap 9-41
 PHStat | One-Sample Tests | t Test for the
Mean, Sigma Known …
 Example in Excel Spreadsheet
t Test: Unknown in PHStatσ

42. © 2003 Prentice-Hall, Inc.
Chap 9-42
Proportion
 Involves Categorical Variables
 Two Possible Outcomes
 “Success” (possesses a certain characteristic) and
“Failure” (does not possess a certain characteristic)
 Fraction or Proportion of Population in the
“Success” Category is Denoted by p

43. © 2003 Prentice-Hall, Inc.
Chap 9-43
Proportion
 Sample Proportion in the Success Category is
Denoted by pS

 When Both np and n(1-p) are at Least 5, pS
Can Be Approximated by a Normal
Distribution with Mean and Standard
Deviation

(continued)
Number of Successes
Sample Size
s
X
p
n
= =
sp pµ =
(1 )
sp
p p
n
σ
−
=

44. © 2003 Prentice-Hall, Inc.
Chap 9-44
Example: Z Test for Proportion
( )
( ) ( )
Check:
500 .04 20
5
1 500 1 .04
480 5
np
n p
= =
≥
− = −
= ≥
A marketing company
claims that a survey will
have a 4% response rate.
To test this claim, a
random sample of 500
were surveyed with 25
responses. Test at the α =
.05 significance level.

45. © 2003 Prentice-Hall, Inc.
Chap 9-45
Reject and Do Not Reject
Regions
Z0 1.96
.025
Reject
-1.96
.025
1.1411
0.04SP pµ = = 0.05
Reject
0 : 0.04H p =
1 : 0.04H p ≠
SP

46. © 2003 Prentice-Hall, Inc.
Chap 9-46
0.05
Critical Values: ± 1.96
1.1411
( ) ( )
.05 .04
1.1411
1 .04 1 .04
500
Sp p
Z
p p
n
− −
≅ = =
− −
Z Test for Proportion: Solution
α = .05
n = 500
Do not reject at α = .05.
H0: p = .04
H1: p ≠ .04
Test
Statistic:
Decision
:
Conclusion
:
Z
0
Reject Reject
.025.025
1.96-1.96
We do not have sufficient
evidence to reject the
company’s claim of 4%
response rate.
SP
0.04

47. © 2003 Prentice-Hall, Inc.
Chap 9-47
p -Value Solution
(p-Value = 0.2538) ≥ (α = 0.05)
Do Not Reject.
0 1.1411
Z
Reject
α = 0.05
1.96
p-Value = 2 x .1269
Test Statistic 1.1411 is in the Do Not Reject
Region
Reject

48. © 2003 Prentice-Hall, Inc.
Chap 9-48
Z Test for Proportion in PHStat
 PHStat | One-Sample Tests | Z Test for the
Proportion …
 Example in Excel Spreadsheet

49. © 2003 Prentice-Hall, Inc.
Chap 9-49
Potential Pitfalls and
Ethical Issues
 Data Collection Method is Not Randomized to
Reduce Selection Biases
 Treatment of Human Subjects are
Manipulated Without Informed Consent
 Data Snooping is Used to Choose between
One-Tail and Two-Tail Tests, and to
Determine the Level of Significance

50. © 2003 Prentice-Hall, Inc.
Chap 9-50
Potential Pitfalls and
Ethical Issues
 Data Cleansing is Practiced to Hide
Observations that do not Support a Stated
Hypothesis
 Fail to Report Pertinent Findings
(continued)

51. © 2003 Prentice-Hall, Inc.
Chap 9-51
Chapter Summary
 Addressed Hypothesis Testing Methodology
 Performed Z Test for the Mean ( Known)
 Discussed p –Value Approach to Hypothesis
Testing
 Made Connection to Confidence Interval
Estimation
σ

52. © 2003 Prentice-Hall, Inc.
Chap 9-52
Chapter Summary
 Performed One-Tail and Two-Tail Tests
 Performed t Test for the Mean ( Unknown)
 Performed Z Test for the Proportion
 Discussed Potential Pitfalls and Ethical Issues
(continued)
σ