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Statistics. Hypothesis Tests. Contents. Developing Null and Alternative Hypotheses Type I and Type II Errors Population Mean: σ Known Population Mean: σ Unknown Population Proportion Hypothesis Testing and Decision Making

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### Statistics

Hypothesis Tests

• Developing Null and Alternative Hypotheses

• Type I and Type II Errors

• Population Mean: σ Known

• Population Mean: σUnknown

• Population Proportion

• Hypothesis Testing and Decision Making

• Calculating the Probability of Type II Errors

• Determining the Sample Size for

• Hypothesis Tests About a Population Mean

• Population Proportion

• Hypothesis Testing and Decision Making

• Calculating the Probability of Type II Errors

• Determining the Sample Size for

• Hypothesis Tests About a Population Mean

STATISTICSin PRACTICE

• John Morrell & Company is considered

the oldest continuously operating meat

manufacturer in the United States.

• Market research at Morrell provides management with up to- date information on the company’s various products and how the products compare with competing brands of similar products.

STATISTICSin PRACTICE

• One research question concerned whether Morrell’s Convenient Cuisine Beef Pot Roast was the preferred choice of more than 50% of the consumer population. The hypothesis test for the research question is Ho: p ≤ .50 vs. Ha: p > .50

• In this chapter we will discuss how to formulate hypotheses and how to conduct tests like the one used by Morrell.

• Hypothesis testing can be used to determine

• whether a statement about the value of a

• population parameter should or should not be

• rejected.

• The null hypothesis, denoted by Ho , is a

• tentative assumption about a population

• parameter.

• The alternative hypothesis, denoted by Ha,

• is the opposite of what is stated in the null

• hypothesis.

• The alternative hypothesis is what the test is

attempting to establish.

• Testing Research Hypotheses

• Testing the Validity of a Claim

• Testing in Decision-Making Situations

• Testing Research Hypotheses

• The research hypothesis should be expressed

• as the alternative hypothesis.

• The conclusion that the research hypothesis

• is true comes from sample data that contradict

• the null hypothesis.

• Testing Research Hypotheses

Example:

Consider a particular automobile model that

currently attains an average fuel efficiency of 24 miles per gallon.

A product research group developed a new

fuel injection system specifically designed to

increase the miles-per-gallon rating.

The appropriate null and alternative hypotheses

for the study are:

Ho: μ ≤ 24

Ha: μ > 24

• Testing Research Hypotheses

• In research studies such as these, the null and

• alternative hypotheses should be formulated

• so that the rejection of H0 supports the research conclusion. The research hypothesis therefore should be expressed as the alternative hypothesis.

• Testing the Validity of a Claim

• Manufacturers’ claims are usually given the

• benefit of the doubt and stated as the null

• hypothesis.

• The conclusion that the claim is false comes

• from sample data that contradict the null

• hypothesis.

• Testing the Validity of a Claim

Example:

Consider the situation of a manufacturer of soft

drinks who states that it fills two-liter containers

of its products with an average of at least 67.6

fluid ounces.

• Testing the Validity of a Claim

A sample of two-liter containers will be selected,

and the contents will be measured to test the

manufacturer’s claim.

The null and alternative hypotheses as follows.

Ho: μ ≥ 67. 6

Ha: μ < 67. 6

• Testing the Validity of a Claim

• In any situation that involves testing the validity

• of a claim, the null hypothesis is generally based

• on the assumption that the claim is true. The

• alternative hypothesis is then formulated so that

• rejection of H0will provide statistical evidence

• that the stated assumption is incorrect.

• Action to correct the claim should be considered

• whenever H0 is rejected.

• Testing in Decision-Making Situations

• A decision maker might have to choose

• betweentwo courses of action, one

• associated with the null hypothesis and

• another associated with the alternative

• hypothesis.

• Testing in Decision-Making Situations

Example:

Accepting a shipment of goods from a supplier

or returning the shipment of goods to the supplier.

Assume that specifications for a particular part

require a mean length of two inches per part.

The null and alternative hypotheses would be

formulated as follows.

H0 : μ = 2

Ha: μ≠ 2

• Testing in Decision-Making Situations

• If the sample results indicate H0 cannot be rejected and the shipment will be accepted.

• If the sample results indicate H0 should be rejected the parts do not meet specifications. The quality control inspector will have sufficient evidence to return the shipment to the supplier.

• Testing in Decision-Making Situations

• We see that for these types of situations, action is taken both when H0cannot be rejected and when H0 can be rejected.

Summary of Forms for Null and Alternative Hypotheses about a Population Mean

• The equality part of the hypotheses always

• appears in the null hypothesis.(Follows Minitab)

• In general, a hypothesis test about the value of

• apopulation mean  must take one of the

• followingthree forms (where 0is the

• hypothesized value of the population mean).

Summary of Forms for Null and Alternative Hypotheses about a Population Mean

One-tailed

(lower-tail)

One-tailed

(upper-tail)

Two-tailed

Null and Alternative Hypotheses Population Mean

• Example: Metro EMS

A major west coast city provides

one of the most comprehensive

emergency medical services in

the world.

Operating in a multiple

hospital system with

approximately 20 mobile medical

units, the service goal is to respond to medical

emergencies with a mean time of 12 minutes or less.

Null and Alternative Hypotheses Population Mean

• Example: Metro EMS

The director of medical services

wants to formulate a hypothesis

test that could use a sample of

emergency response times to

determine whether or not the

service goal of 12 minutes or less

is being achieved.

Null and Alternative Hypotheses Population Mean

The emergency service is meeting

the response goal; no follow-up

action is necessary.

H0: 

The emergency service is not

meeting the response goal;

appropriate follow-up action is

necessary.

Ha:

where: μ = mean response time for the population

of medical emergency requests

Type I and Type II Errors Population Mean

• Because hypothesis tests are based on sample

data, we must allow for the possibility of errors.

• A Type I error is rejecting H0 when it is true.

• Applications of hypothesis testing that only

control the Type I error are often called

significance tests.

Type I and Type II Errors Population Mean

Type I error

• The probability of making a Type I error when the null hypothesis is true as an equality is called the level of significance.

• The Greek symbol α(alpha) is used to denote the level of significance, and common choices for α are .05 and .01.

Type I and Type II Errors Population Mean

Type I error

• If the cost of making a Type I error is high, small values of α are preferred. If the cost of making a Type I error is not too high, larger values of α are typically used.

Type I and Type II Errors Population Mean

Type II error

• A Type II error is accepting H0 when it is false.

• It is difficult to control for the probability of

• making a Type II error. Most applications of

• hypothesis testing control for the probability

• of making a Type I error.

• Statisticians avoid the risk of making a Type II

error by using “do not reject H0” and not

“accept H0”.

Type I and Type II Errors Population Mean

Population Condition

H0 True

(m< 12)

H0 False

(m > 12)

Conclusion

Not rejectH0

(Concludem< 12)

Correct

Decision

Type II Error

Type I Error

Correct

Decision

RejectH0

(Conclude m > 12)

Steps of Hypothesis Testing Population Mean

Step 1.Develop the null and alternative hypotheses.

Step 2.Specify the level of significance α .

Step 3.Collect the sample data and compute

the test statistic.

p-Value Approach

Step 4.Use the value of the test statistic to compute

the p-value.

Step 5.Reject H0 if p-value <α.

Steps of Hypothesis Testing Population Mean

Critical Value Approach

Step 4. Use the level of significance to determine the critical value and the rejection rule.

Step 5. Use the value of the test statistic and the rejection rule to determine whether to

reject H0.

p Population Mean-Value Approach to

One-Tailed Hypothesis Testing

• The p-value is the probability, computed using

the test statistic, that measures the support

(or lack of support) provided by the sample

for the null hypothesis. The p-value is also called

the observed level of significance.

• If the p-value is less than or equal to the level

of significance α , the value of the test statistic

is in the rejection region.

• Reject H0 if the p-value <α .

Critical Value Approach to Population Mean

One-Tailed Hypothesis Testing

• The test statistic z has a standard normal

probability distribution.

• We can use the standard normal probability

• distribution table to find the z-value with an

• area of α in the lower (or upper) tail of the

• distribution.

Critical Value Approach to Population Mean

One-Tailed Hypothesis Testing

• The value of the test statistic that established

• theboundary of the rejection region is called

• thecritical value for the test.

• The rejection rule is:

• Lower tail: Reject H0 if z< - z α

• Upper tail: Reject H0 if z>zα 

One-Tailed Hypothesis Testing Population Mean

• Population Mean: σ Known

• One-tailed testsabout a population mean take one of the following two forms.

Lower Tail Test Upper Tail Test

Sampling Population Mean

distribution

of

Lower-Tailed Test About a Population Mean:sKnown

p-Value <a,

so reject H0.

• p-Value Approach

a = .10

p-value

72

z

z =

-1.46

-za =

-1.28

0

Sampling Population Mean

distribution

of

Lower-Tailed Test About a Population Mean : sKnown

p-Value <a,

so reject H0.

• p-Value Approach

a = .04

p-Value

11

z

za =

1.75

z =

2.29

0

Sampling Population Mean

distribution

of

Lower-Tailed Test About a Population Mean : sKnown

• Critical Value Approach

Reject H0

a 1

Do Not Reject H0

z

-za = -1.28

0

Sampling Population Mean

distribution

of

Upper-Tailed Test About a Population Mean : sKnown

• Critical Value Approach

Reject H0



Do Not Reject H0

z

za = 1.645

0

One-Tailed Hypothesis Testing Population Mean

Example:

• The label on a large can of Hilltop Coffee states that the can contains 3 pounds of coffee. The Federal Trade Commission (FTC) knows that Hilltop’s production process cannot place exactly 3 pounds of coffee in each can, even if the mean filling weight for the population of all cans filled is 3 pounds per can.

One-Tailed Hypothesis Testing Population Mean

Example:

• However, as long as the population mean filling weight is at least 3 pounds per can, the rights of consumers will be protected.

One-Tailed Hypothesis Testing Population Mean

Step 1. Develop the null and alternative hypotheses.

denoting the population mean filling weight and

the hypothesized value of the population mean is

μ0 = 3. The null and alternative hypotheses are

H0 : μ ≥ 3

Ha: μ < 3

Step 2. Specify the level of significance α.

we set the level of significance for the hypothesis

test at α= .01

One-Tailed Hypothesis Testing Population Mean

Step 3. Collect the sample data and compute the

test statistic.

Test statistic:

Sample data: σ = .18 and sample size n=36,

= 2.92 pounds.

One-Tailed Hypothesis Testing Population Mean

Step 3. Collect the sample data and compute the

test statistic.

We have z = -2.67

One-Tailed Hypothesis Testing Population Mean

• p-Value Approach

Step 4. Use the value of the test statistic to compute the p-value.

Using the standard normal distribution table, the

area between the mean and z = - 2.67 is .4962.

The p-value is .5000 - .4962 = .0038,

Step 5. p-value = .0038 < α = .01

Reject H0.

One-Tailed Hypothesis Testing Population Mean

• p-value for The Hilltop Coffee Study when

sample mean = 2.92 and z = -2 .67.

One-Tailed Hypothesis Testing Population Mean

Critical Value Approach

Step 4. Use the level of significance to determine the critical value and the rejection rule.

The critical value is z = -2.33.

One-Tailed Hypothesis Testing Population Mean

Step 5. Use the value of the test statistic and the rejection rule to determine whether to reject H0.

We will reject H0 if z <- 2.33.

Because z = - 2.67 < 2.33, we can reject H0 and conclude that Hilltop Coffee is under filling cans.

One-Tailed Tests About a Population Population MeanMean : sKnown

• Example: Metro EMS

The response times for a random

sample of 40 medical emergencies

were tabulated. The sample mean

is 13.25 minutes. The population

standard deviation is believed to

be 3.2 minutes.

One-Tailed Tests About a Population Population MeanMean : sKnown

• Example: Metro EMS

The EMS director wants to

perform a hypothesis test, with a

.05 level of significance, to determine

whether the service goal of 12 minutes or less

is being achieved.

One-Tailed Tests About a Population Population MeanMean: sKnown

• p -Value and Critical Value Approaches

1. Develop the hypotheses.

H0: 

Ha:

a = .05

2. Specify the level of significance.

3. Compute the value of the test statistic.

One-Tailed Tests About a Population Population MeanMean: sKnown

• p –Value Approach

4. Compute the p –value.

For z = 2.47, cumulative probability = .9932.

p–value = 1 - .9932 = .0068

5. Determine whether to reject H0.

Because p–value = .0068 <α = .05, we reject H0.

We are at least 95% confident that Metro EMS is not meeting the response goal of 12 minutes.

Sampling Population Mean

distribution

of

One-Tailed Tests About a Population Mean: sKnown

• p –Value Approach

a = .05

p-value



z

za =

1.645

z =

2.47

0

One-Tailed Tests About a Population Population MeanMean: sKnown

• Critical Value Approach

4. Determine the critical value and rejection rule.

For α = .05, z.05 = 1.645

Reject H0 if z> 1.645

5. Determine whether to reject H0.

Because 2.47 > 1.645, we reject H0.

We are at least 95% confident that Metro EMS is not meeting the response goal of 12 minutes.

Two-Tailed Hypothesis Testing Population Mean

• In hypothesis testing, the general form for a two-tailed test about a population mean is as follows:

p Population Mean-Value Approach to

Two-Tailed Hypothesis Testing

• Compute the p-value using the following three

steps:

1. Compute the value of the test statistic z.

2. If z is in the upper tail (z > 0), find the area

under the standard normal curve to the right

of z. If z is in the lower tail (z < 0), find the

area under the standard normal curve to the

left of z.

p Population Mean-Value Approach to

Two-Tailed Hypothesis Testing

3. Double the tail area obtained in step 2 to obtain

the p –value.

• The rejection rule:

• Reject H0 if the p-value <.

Critical Value Approach to Population Mean

Two-Tailed Hypothesis Testing

• The critical values will occur in both the lower

and upper tails of the standard normal curve.

• Use the standard normal probability

distribution table to find za/2 (the z-value

with an area of a/2 in the upper tail of

the distribution).

• The rejection rule is:

• Reject H0 if z< -za/2 or z>za/2

Two-Tailed Hypothesis Testing Population Mean

• Example: The U.S. Golf Association (USGA) establishes rules that manufacturers of golf equipment must meet if their products are to be acceptable for use in USGA events.

• MaxFlight produce golf balls with an average distance of 295 yards.

Two-Tailed Hypothesis Testing Population Mean

• When the average distance falls below 295 yards, the company worries about losing sales because the golf balls do not provide as much distance as advertised.

Two-Tailed Hypothesis Testing Population Mean

• When the average distance passes 295 yards, MaxFlight’s golf balls may be rejected by the

USGA for exceeding the overall distance

standard concerning carry and roll.

• A hypothesized value of μ0= 295 and the null and alternative hypotheses test are as follows:

H0 : μ = 295 VS Ha : μ≠ 295

Two-Tailed Hypothesis Testing Population Mean

• If the sample mean is significantly less than

295 yards or significantly greater than 295 yards, we will reject H0.

Two-Tailed Hypothesis Testing Population Mean

Step 1. Develop the null and alternative

hypotheses.

A hypothesized value of μ0 = 295 and

H0 : μ = 295

Ha : μ≠ 295

Step 2. Specify the level of significance α.

we set the level of significance for the hypothesis test at α = .05

Two-Tailed Hypothesis Testing Population Mean

Step 3. Collect the sample data and compute the

test statistic.

Test statistic:

Sample data: σ = 12 and sample size n=50,

= 297.6 yards.

We have

Two-Tailed Hypothesis Testing Population Mean

• p-Value Approach

Step 4. Use the value of the test statistic to compute the p-value.

The two-tailed p-value in this case is given by

P(z< -1.53) + P(z> 1.53)= 2(.0630) = .1260.

Step 5.We do not reject H0 because the

p-value = .1260 > .05. No action will be taken

Two-Tailed Hypothesis Testing Population Mean

• p-Value for The Maxflight Hypothesis Test

Two-Tailed Hypothesis Testing Population Mean

Critical Value Approach

Step 4. Use the level of significance to determine the critical value and the rejection rule. The critical values are - z.025 = -1.96 and z.025 = 1.96.

Two-Tailed Hypothesis Testing Population Mean

Step 5. Use the value of the test statistic and the rejection rule to determine whether to reject H0.

We will reject H0 if z < - 1.96 or if z > 1.96

Because the value of the test statistic is z=1.53, the statistical evidence will not permit us to reject the null hypothesis

at the .05 level of significance.

oz. Population Mean

Glow

Example: Glow Toothpaste

• Two-Tailed Test About a Population Mean: s Known

The production line for Glow toothpaste

is designed to fill tubes with a mean weight

of 6 oz. Periodically, a sample of 30 tubes

will be selected in order to check the

filling process.

oz. Population Mean

Glow

Example: Glow Toothpaste

• Two-Tailed Test About a Population Mean: s Known

Quality assurance procedures call for

the continuation of the filling process if the

sample results are consistent with the assumption

that the mean filling weight for the population of

toothpaste

tubes is 6 oz.; otherwise the process will be

oz. Population Mean

Glow

Example: Glow Toothpaste

• Two-Tailed Test About a Population Mean: s Known

Assume that a sample of 30 toothpaste tubes

provides a sample mean of 6.1 oz. The population

standard deviation is believed to be 0.2 oz.

Perform a hypothesis test, at the .03

level of significance, to help determine

whether the filling process should continue

operating or be stopped and corrected.

Glow Population Mean

1. Determine the hypotheses.

Two-Tailed Tests About a Population Mean:sKnown

• p –Value and Critical Value Approaches

α = .03

2. Specify the level of significance.

3. Compute the value of the test statistic.

Glow Population Mean

Two-Tailed Tests About a Population Mean : sKnown

• p –Value Approach

4. Compute the p –value.

For z = 2.74, cumulative probability = .9969

p–value = 2(1 - .9969) = .0062

5. Determine whether to reject H0.

Because p–value = .0062 < α = .03, we reject H0.

We are at least 97% confident that the mean filling weight of the toothpaste tubes is not 6 oz.

Glow Population Mean

Two-Tailed Tests About a Population Mean : sKnown

• p-Value Approach

1/2

p -value

= .0031

1/2

p -value

= .0031

a/2 =

.015

a/2 =

.015

z

z = -2.74

0

z = 2.74

-za/2 = -2.17

za/2 = 2.17

Glow Population Mean

Two-Tailed Tests About a Population Mean : sKnown

• Critical Value Approach

4. Determine the critical value and rejection rule.

For a/2 = .03/2 = .015, z.015 = 2.17

Reject H0 if z< -2.17 or z> 2.17

5. Determine whether to reject H0.

Because 2.47 > 2.17, we reject H0.

We are at least 97% confident that the mean filling weight of the toothpaste tubes is not 6 oz.

Glow Population Mean

Sampling

distribution

of

Two-Tailed Tests About a Population Mean : σKnown

• Critical Value Approach

Reject H0

Reject H0

Do Not Reject H0

a/2 = .015

a/2 = .015

z

0

2.17

-2.17

Summary of Hypothesis Tests about a Population MeanPopulation Mean: σ Known Case

Confidence Interval Approach to Population MeanTwo-Tailed Tests About a Population Meant

• Select a simple random sample from the

population and use the value of the sample

mean to develop the confidence interval for

the population mean .

(Confidence intervals are covered in Chapter 8.)

• If the confidence interval contains the

hypothesized value 0do not reject H0.

Otherwise, reject H0.

Glow Population Mean

Confidence Interval Approach toTwo-Tailed Tests About a Population Mean

The 97% confidence interval for  is

or 6.02076 to 6.17924

Because the hypothesized value for the

population mean,0=6, is not in this interval,

the hypothesis-testing conclusion is that the

null hypothesis,H0: = 6, can be rejected.

Tests About a Population Mean: Population Means Unknown

• Test Statistic

This test statistic has a t distribution

with n - 1 degrees of freedom.

Tests About a Population Mean: Population Means Unknown

• Rejection Rule: p -Value Approach

Reject H0 if p –value <a

• Rejection Rule: Critical Value Approach

Reject H0 if t<-t

H0: 

H0: 

Reject H0 if t>t

H0: 

Reject H0 if t < - tor t >t

p Population Mean-Values and the t Distribution

• The format of the t distribution table provided in

most statistics textbooks does not have sufficient

detail to determine the exactp-value p-value for

a hypothesis test.

• However, we can still use the t distribution table

to identify a range for the p-value.

• An advantage of computer software packages is

that the computer output will provide the p-value

for the t distribution.

Tests About a Population Mean: Population Means Unknown

• Example: A business travel magazine wants to classify transatlantic gateway airports according to the mean rating for the population of business travelers.

• A rating scale with a low score of 0 and a high score of 10 will be used, and airports with a population mean rating greater than 7 will be designated as superior service airports.

Tests About a Population Mean: Population MeansUnknown

• The null and alternative hypotheses for this upper tail test are as follows:

H0 : μ ≤ 7

Ha : μ > 7

Tests About a Population Mean: Population MeansUnknown

• Test Statistic

The sampling distribution

of t has n – 1 df.

We have =7.25, s = 1.052, and n = 60, and the value of the test statistic is

• p Population Mean-Value Approach

Using Table 2 in Appendix B, the t distribution with 59 degrees of freedom.

• We see that t 1.84 is between 1.671 and 2.001. Although the table does not provide the exact p-value, the values in the “Area in Upper Tail” row show that the p-value must be less than .05 and greater than .025.

• MINITAB OUTPUT Population Mean

• The test statistic t = 1.84, and the exact

p-value is .035 for the Heathrow rating hypothesis test.

• A p-value = .035 < .05 leads to the rejection of the null hypothesis.

• Critical Value Approach Population Mean

The rejection rule is thus

Reject H0 if t >1.671

With the test statistic t = 1.84 > 1.671, H0is rejected.

Example: Highway Patrol Population Mean

• One-Tailed Test About a Population Mean: s Unknown

A State Highway Patrol periodically samples

vehicle speeds at various locations

The sample of vehicle speeds

is used to test the hypothesis

H0: m< 65

The locations where H0 is rejected are deemed

the best locations for radar traps.

Example: Highway Patrol Population Mean

• One-Tailed Test About a Population Mean: s Unknown

At Location F, a sample of 64 vehicles shows a

mean speed of 66.2 mph with a

standard deviation of

4.2 mph. Use a = .05 to

test the hypothesis.

One-Tailed Test About a Population MeanPopulation Mean : s Unknown

• p –Value and Critical Value Approaches

1. Determine the hypotheses.

H0: < 65

Ha: m> 65

a= .05

2. Specify the level of significance.

3. Compute the value of the test statistic.

One-Tailed Test About a Population Population MeanMean : s Unknown

• p –Value Approach

4. Compute the p –value.

For t = 2.286, the p–value must be less than .025 (for t = 1.998) and greater than .01 (for t = 2.387).

.01 < p–value < .025

One-Tailed Test About a Population Population MeanMean: s Unknown

• p –Value Approach

5. Determine whether to reject H0.

Because p–value <a = .05, we reject H0.

We are at least 95% confident that the mean speed of vehicles at Location F is greater than 65 mph.

One-Tailed Test About a Population Population MeanMean: s Unknown

• Critical Value Approach

4. Determine the critical value and rejection rule.

For a = .05 and d.f. = 64 – 1 = 63, t.05 = 1.669

Reject H0 if t> 1.669

One-Tailed Test About a Population Population MeanMean: s Unknown

• Critical Value Approach

5. Determine whether to reject H0.

Because 2.286 > 1.669, we reject H0.

We are at least 95% confident that the mean speed of vehicles at Location F is greater than 65 mph. Location F is a good candidate for a radar trap.

One-Tailed Test About a Population Population MeanMean: s Unknown

Reject H0

Do Not Reject H0



t

ta =

1.669

0

Tests About a Population Mean: Population Means Unknown

• Summary of Hypothesis Tests about a Population Mean: s Unknown Case

A Summary of Forms for Null and Alternative Hypotheses About a Population Proportion

• The equality part of the hypotheses always

• appearsin the null hypothesis.

• In general, a hypothesis test about the value of

• a population proportion pmust take one of the

following three forms (where p0 is the

hypothesizedvalue of the population

proportion).

A Summary of Forms for Null and Alternative Hypotheses About a Population Proportion

One-tailed

(lower tail)

One-tailed

(upper tail)

Two-tailed

Tests About a Population Proportion a Population Proportion

• Test Statistic

where:

assuming np> 5 and n(1 – p)> 5

Tests About a Population Proportion a Population Proportion

• Rejection Rule: p –Value Approach

Reject H0 if p –value < α

• Rejection Rule: Critical Value Approach

H0: pp

Reject H0 if z>z

H0: pp

Reject H0 if z< -z

H0: pp

Reject H0 if z< -z or z>z

Upper Tail Test About a a Population ProportionPopulation Proportion

• Example: Pine Creek golf course

Over the past year, 20% of the players at Pine Creek were women.

A special promotion designed to attract women golfers. One month after the promotion was implemented.

• The null and alternative hypotheses are as

follows: H0: p ≤ .20

Ha: p > .20

Upper Tail Test About a a Population ProportionPopulation Proportion

• level of significance of α = .05 be used.

• Test statistics

Upper Tail Test About a a Population ProportionPopulation Proportion

• level of significance of α = .05 be used.

• Suppose a random sample of 400 players was

selected, and that 100 of the players were women.

The proportion of women golfers in the

sample is

and the value of the test statistic is

Upper Tail Test About a a Population ProportionPopulation Proportion

p-Value Approach

• the p-value is the probability that z is greater than or equal to z = 2.50. Thus, the p-value for the test is .5000 – .4938 = .0062.

Upper Tail Test About a a Population ProportionPopulation Proportion

• A p-value = .0062 < .05 gives sufficient statistical evidence to reject H0 at the .05 level of significance.

Critical Value Approach

• The critical value corresponding to an area of .05 in the upper tail of a standard normal distribution is z.05 = 1.645. To reject H0 if

z ≥ 1.645.

• Because z = 2.50 > 1.645, H0 is rejected.

Two-Tailed Test About a a Population Proportion

Population Proportion

• Example: National Safety Council

For a Christmas and New Year’s week, the

National Safety Council estimated that

500 people would be killed and 25,000

injured on the nation’s roads. The

NSC claimed that 50% of the

accidents would be caused by

drunk driving.

Two-Tailed Test About a a Population Proportion

Population Proportion

• Example: National Safety Council

A sample of 120 accidents showed that

67 were caused by drunk driving. Use

these data to test the NSC’s claim with

α = .05.

Two-Tailed Test About a a Population Proportion

Population Proportion

• p –Value and Critical Value Approaches

1. Determine the hypotheses.

2. Specify the level of significance.

a = .05

a common a Population Proportion

error is using in this formula

Population Proportion

• p –Value and Critical Value Approaches

3. Compute the value of the test statistic.

Two-Tailed Test About a a Population Proportion

Population Proportion

• p -Value Approach

4. Compute the p -value.

For z = 1.28, cumulative probability = .8997

p–value = 2(1 - .8997) = .2006

5. Determine whether to reject H0.

Because p–value = .2006 > a = .05, we cannot reject H0.

Two-Tailed Test About a a Population Proportion

Population Proportion

• Critical Value Approach

4. Determine the critical value and rejection rule.

Fora/2 = .05/2 = .025, z.025 = 1.96

Reject H0 if z < -1.96 or z > 1.96

5. Determine whether to reject H0.

Because 1.278 > -1.96 and < 1.96, we cannot reject H0.

Tests About a Population Proportion a Population Proportion

• Summary of Hypothesis Tests about a Population Proportion

Hypothesis Testing and Decision Making a Population Proportion

• In many decision-making situations the

• decision maker may want, and in some cases

• may be forced, to take action with both the

• conclusion do not rejectH0 and the conclusion rejectH0.

• In such situations, it is recommended that

• the hypothesis-testing procedure be extended

• to include consideration of making a Type II

• error.

Hypothesis Testing and Decision Making a Population Proportion

• Example:

A quality control manager must decide to accept a shipment of batteries from a supplier or to return the shipment because of poor quality.

Suppose the null and alternative hypotheses about the population mean follow.

H0 : μ ≥ 120

Ha : μ < 120

Hypothesis Testing and Decision Making a Population Proportion

• If H0 is rejected, the appropriate action is to return the shipment to the supplier.

• If H0 is not rejected, the decision maker must

still determine what action should be taken.

• In such decision-making situations, it is recommended to control the probability of making a Type II error. Because knowledge

of the probability of making a Type II error

Calculating the Probability of a Type II Error a Population Proportionin Hypothesis Tests About a Population Mean

1. Formulate the null and alternative hypotheses.

• Using the critical value approach, use the level

• of significance to determine the critical value

• and the rejection rule for the test.

• Using the rejection rule, solve for the value of

• the sample mean corresponding to the critical

• value of the test statistic.

5. Using the sampling distribution of a Population Proportionfor a value

of μ satisfying the alternative hypothesis, and

the acceptance region from step 4, compute the probability that the sample mean will be in the acceptance region. (This is the probability of making a Type II error at the chosen

level of μ.)

Calculating the Probability of a Type II Error in Hypothesis Tests About a Population Mean

4. Use the results from step 3 to state the values of the sample mean that lead to the acceptance of H0; this defines the acceptance region.

Calculating the Probability of a Type II Error a Population Proportionin Hypothesis Tests About a Population Mean

• Example: Batteries’ Quality

• Suppose the null and alternative hypotheses are

H0 : μ ≥ 120

Ha : μ < 120

• level of significance of α = .05.

• Sample size n=36 and σ = 12 hours.

Calculating the Probability of a Type II Error a Population Proportionin Hypothesis Tests About a Population Mean

• The critical value approach and z.05 = 1.645, the rejection rule is

Reject H0 if z ≤ -1.645

Calculating the Probability of a Type II Error a Population Proportionin Hypothesis Tests About a Population Mean

• The rejection rule indicates that we will reject H0 if

That indicates that we will reject H0if

and accept the shipment whenever > 116.71.

Calculating the Probability of a Type II Error a Population Proportionin Hypothesis Tests About a Population Mean

• Compute probabilities associated with

making a Type II error (whenever the true

mean is less than 120 hours and we make

the decision to accept H0: μ ≥120.)

Calculating the Probability of a Type II Error a Population Proportionin Hypothesis Tests About a Population Mean

• If μ =112 is true, the probability of making a Type II error is the probability that the sample mean is greater than 116.71 when μ = 112,that is, P( ≥ 116.71 | μ = 112) =?

• The probability of making a Type II error (β ) when μ = 112 is .0091 .

Calculating the Probability of a Type II Error a Population Proportionin Hypothesis Tests About a Population Mean

• We can repeat these calculations for other values of μ less than 120.

Calculating the Probability of a Type II Error a Population Proportionin Hypothesis Tests About a Population Mean

• The powerof the test = the probability of correctly rejecting H0 when it is false. For any particular value of μ, the power is 1-β.

• Power Curve for The Lot-Acceptance Hypothesis Test

Calculating the Probability a Population Proportion of a Type II Error

• Example: Metro EMS (revisited)

Recall that the response times for

a random sample of 40 medical

emergencies were tabulated. The

sample mean is 13.25 minutes.

The population standard deviation

is believed to be 3.2 minutes.

Calculating the Probability a Population Proportion of a Type II Error

• Example: Metro EMS (revisited)

The EMS director wants to

perform a hypothesis test, with a

.05 level of significance, to determine

whether or not the service goal of 12 minutes or less is being achieved.

4. We will accept a Population ProportionH0when < 12.8323

Calculating the Probability of a Type II Error

1.Hypotheses are:H0:μandHa:μ

2. Rejection rule is: RejectH0 if z>1.645

• 3.Value of the sample mean that identifies

• the rejection region:

• Values of a Population Proportionm b 1-b

Calculating the Probability of a Type II Error

5.Probabilities that the sample mean will be

in the acceptance region:

14.0 -2.31 .0104 .9896

13.6 -1.52 .0643 .9357

13.2 -0.73 .2327 .7673

12.8323 0.00 .5000 .5000

12.8 0.06 .5239 .4761

12.4 0.85 .8023 .1977

12.0001 1.645 .9500 .0500

Example: a Population Proportionm = 12.0001, b= .9500

Calculating the Probability of a Type II Error

• Calculating the Probability of a Type II Error

• When the true population mean m is close to

• the null hypothesis value of 12, there is a high

• probability that we will make a Type II error.

Example a Population Proportion: m = 14.0, b = .0104

Calculating the Probability of a Type II Error

• Calculating the Probability of a Type II Error

• When the true population mean m is far above the null hypothesis value of 12, there is a low probability that we will make a Type II error.

Power of the Test a Population Proportion

• The probability of correctly rejecting H0

• when it is false is called the power of the test.

• For any particular value of μ, the power is

• 1 – β.

• We can show graphically the power

• associated with each value of μ; such a graph

• is called a power curve. (See next slide.)

Power Curve a Population Proportion

H0 False

m

Determining the Sample Size for a Hypothesis Test About a Population Mean

• The specified level of significance determines the probability of making a Type I error.

• By controlling the sample size, the probability

• of making a Type II error is controlled.

Determining the Sample Size for a Hypothesis Test About a Population Mean

• Example: how a sample size can be determined for the lower tail test about a population mean.

H0 : μ ≥ μ0

Ha : μ < μ0

• Let α be the probability of a Type I error and zαand zβare the z value corresponding to an area of α and β, respectively in the upper tail of the standard normal distribution.

Determining the Sample Size for a Hypothesis Test About a Population Mean

• we compute c using the following formulas

Determining the Sample Size for a Hypothesis Test About a Population Mean

• To determine the required sample size, we solve for the n as follows.

c Population Mean

c

Determining the Sample Size for a Hypothesis Test About a Population Mean

H0: m0

Ha:m0

Reject H0

a

m0

Sampling

Distribution of when

H0 is true

and m = m0

Sampling

distribution

of when

H0 is false

and ma > m0

b

ma

Determining the Sample Size for a Hypothesis Test About a Population Mean

where

z= z value providing an area of  in the tail

z= z value providing an area of in the tail

 = population standard deviation

0 = value of the population mean in H0

a = value of the population mean used for theType II error

Note: In a two-tailed hypothesis test, use z /2 not z

Relationship Among Population Meana, b, and n

• Once two of the three values are known, the other can be computed.

• For a given level of significance a, increasing the sample size n will reduce b.

• For a given sample size n, decreasing a will increaseb, whereas increasing a will decrease b.

Determining the Sample Size for a Hypothesis Test About a Population Mean

• Let’s assume that the director of medical

services makes the following statements

about the allowable probabilities for the Type I and Type II errors:

• If the mean response time is m = 12 minutes, I am willing to risk an a = .05 probability of rejecting H0.

• If the mean response time is 0.75 minutes over the specification (m = 12.75), I am willing to risk a b= .10 probability of not rejecting H0.

Determining the Sample Size for a Hypothesis Test About a Population Mean

Given

a = .05, b = .10

z= 1.645, z= 1.28

0 = 12, a = 12.75

 = 3.2