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CHAPTER 10
HYPOTHESIS TESTING
Outline
• Hypothesis testing
– The context and some terms
– Testing population mean when the population
variance is known
• Two-tail test
• One-tail test
– The p-value of a test of hypothesis
– The probability of a Type II error
1
HYPOTHESIS TESTING
THE CONTEXT
• Example 1: A supervisor of a production line wants to
determine if the production time of a critical part is the same
as its design time, say 100 seconds. A random sample of
parts is taken and their production times are measured.
Does the sample information provide enough evidence that
the production time of the part is 100 seconds? Of course,
both of the following are important
– The production times sampled and
– The size of the sample
• In the above context, hypothesis testing provides a
technique to conclude if the production time of the part is
100 seconds.
2
HYPOTHESIS TESTING
THE CONTEXT
• Example 2: Suppose that a manager wants to produce a
new product if more than 10% potential customers buy the
product. A random sample of potential customers is asked
whether they would buy the product. Does the sample
information provide enough evidence that more than 10%
potential customers will buy the new product? Of course,
both of the following are important
– The yes/no answers provided by the respondents and
– The size of the sample
• In the above context, hypothesis testing provides a
technique to conclude if more than 10% potential
customers will buy the new product.
3
HYPOTHESIS TESTING
THE CONTEXT
• Example 3: Suppose that a quality control inspector wants
to determine if less than 2% items are defective. A random
sample of items are checked and inspected. Does the
sample information provide enough evidence that less than
2% items are defective? Of course, both of the following are
important
– The proportion defectives observed in the sample and
– The size of the sample
• In the above context, hypothesis testing provides a
technique to conclude if less than 2% items are defective.
4
HYPOTHESIS TESTING
SOME TERMS
• Null Hypothesis, HO
– The null hypothesis always specifies a single value. For
example, suppose that it is required to determine if the
population mean is 10. Then, the null hypothesis is
H O :   100
– Note that since the null hypothesis always specifies a
single value, none of the below may be a null hypothesis
H O :   100
H O :   100
H O :   100
H O : 80    120
5
HYPOTHESIS TESTING
SOME TERMS
• Alternative Hypothesis, HA
– The alternative hypothesis is very important because the
conclusion of the hypothesis testing is stated in terms of
alternative hypothesis
– Hypothesis testing provides a technique to determine if
there is enough statistical evidence that the alternative
hypothesis is true.
– There are three forms of alternative hypothesis:
Two-tail test One-tail (right-tail) test One-tail (left-tail) test
H A : p  0.02
H O :   100
H O : p  0.10
H A :   100
H A : p  0.10
H A : p  0.02
Example 1
Example 2
Example 3
6
HYPOTHESIS TESTING
SOME TERMS
• Alternative Hypothesis, HA (One- and Two-Tail Tests)
– It is very important to choose the right form of the
alternative hypothesis. The form depends on the context.
– In Example I, the supervisor wants to know if the mean
is 100 or different from 100. Both the too large and too
small values are equally undesirable. It is appropriate to
reject the claim if the sample mean is much different
from 100. So, the most appropriate test is the two-tail
test.
– In Examples 2 and 3 too small and too large
observations do not lead to the same action. When this
happens, a one-tail test is used.
7
HYPOTHESIS TESTING
SOME TERMS
• Alternative Hypothesis, HA (Left- and Right-Tail Tests)
– Choose between the left- and right-tail tests carefully.
– In Example 2 the manager wants to know if the
proportion is more than 0.10. So, H A : p  0.10 and the
most appropriate test is a right-tail test.
– In Example 3 the inspector wants to know if the
proportion is less than 0.02. So, H A : p  0.02 and the
most appropriate test is a left-tail test.
8
HYPOTHESIS TESTING
SOME TERMS
• Test Statistic and Rejection Region
– The test statistic is computed from the sample data.
– The test statistic is different for different tests. Only z-test
is discussed in Chapter 10 and the test statistic for the ztest is
x 
z
/ n
– The test statistic is the same for both one-tail and twotail tests. The rejection regions for one-tail and two-tail
tests are different.
– If the test statistic lies in the rejection region, the null
hypothesis is rejected, else the null hypothesis is not
rejected (Beware: not rejected  accepted)
9
HYPOTHESIS TESTING
SOME TERMS
• Rejection Region and Level of Significance, 
– Conclusion drawn from sample measurements are
usually expected to contain some errors
– Type I error
• To reject the null hypothesis when the null hypothesis
is actually true!
• Level of significance,  specifies a limit on the
probability of committing Type I error
– Rejection region is different for a different value of 
10
HYPOTHESIS TESTING
SOME TERMS
• Rejection Region
– If the test statistic lies in the rejection region, the null
hypothesis is rejected, else the null hypothesis is not
rejected (not rejected  accepted)
– The rejection regions for z-test are shown below:
z  z / 2
• Two-tail test: reject the null hypothesis if
• Right-tail test: reject the null hypothesis if z  z
• Left-tail test: reject the null hypothesis if
z   z
– Where z is the test statistic,  is the level of significance,
and recall from Chapter 6 that zA is that value of z for
which area on the right is A.
11
HYPOTHESIS TESTING
SOME TERMS
f(x)
• Rejection Region
Two -tail
rejection
region
x 


2
n

2
 z / 2

z / 2
12
HYPOTHESIS TESTING
SOME TERMS
Left -tail
rejection
region
x 

n
f(x)
f(x)
• Rejection Region
Right -tail
rejection
region

x 
n


 z


z
13
HYPOTHESIS TESTING
SOME TERMS
• Type I Error
– Example: Suppose that a manufacturer of packaged
cereals produces cereal boxes. Each box is expected to
have a net weight of 100 gm. Periodically, samples are
collected and the average weight of the sample is
measured. It is possible that although the system is
producing cereal boxes as usual, just because of some
random variation, a sample may contain all boxes with
weights less than 100 gm. Then, the manufacturer may
be tempted to assume some problem with the system,
stop the production and search for the problem. In this
case, the sample data provides a false alarm and a Type
14
I error is committed.
HYPOTHESIS TESTING
SOME TERMS
• Type II Error
– Not to reject the null hypothesis when the null hypothesis
is false! (the opposite of the Type I error). The probability
of committing a Type II error is denoted by .
– Example: consider the manufacturer of the packaged
cereal again. Each cereal box is expected to have a net
weight of 100 gm. But, due to some problems in the
production system, the average weight is shifted to 98
gm. A Type II error is committed if a sample is collected
with average weight nearly 100 gm. Notice that in such a
case, the problem with the production system will not be
detected by the sample!
15
TESTING THE POPULATION MEAN WHEN
THE POPULATION VARIANCE IS KNOWN
A z-test is used in the following context:
• The measurements are normally distributed
• The population standard deviation is known, 
• It is desirable to know if the population mean is
– different from a given value (two-tail test)
– less than a given value (left-tail test)
– more than a given value (right-tail test)
16
TESTING THE POPULATION MEAN WHEN
THE POPULATION VARIANCE IS KNOWN
The test statistic and rejection region for the z-test are:
• Test statistic:
x 
z
/ n
Where, x is the sample mean,  is the population mean
value stated in the null hypothesis,  is the population
standard deviation and n is the sample size.
• Rejection region:
– Two-tail test: reject the null hypothesis if z  z / 2
– Right-tail test: reject the null hypothesis if z  z
– Left-tail test: reject the null hypothesis if z   z
where,  is the level of significance.
17
TESTING THE POPULATION MEAN WHEN
THE POPULATION VARIANCE IS KNOWN
Example 4: A machine that produces ball bearings is set so
that the average diameter is 0.60 inch. In a sample of 49 ball
bearings, the mean diameter was found to be 0.61 inch.
Assuming that the standard deviation is 0.035 can we
conclude at the 5% significance level that the mean diameter
is not 0.50 inch?

HO :
 
HA :
Rejection region:
Test statistic:
Conclusion:
f(x)
x
n

18
HYPOTHESIS TESTING
INTERPRETATION
• If the null hypothesis is rejected
– Conclude that there is enough statistical evidence to
infer that the alternative hypothesis is true
• If the null hypothesis is not rejected
– Conclude that there is not enough statistical evidence to
infer that the alternative hypothesis is true
19
TESTING THE POPULATION MEAN WHEN
THE POPULATION VARIANCE IS KNOWN
Example 5: A random sample of 100 observations from a
normal population whose standard deviation is 50 produced a
mean of 145. Does this statistic provide sufficient evidence at
the 5% significance level to infer that population mean is
more than 140?
HO :
Rejection region:
Test statistic:
Conclusion:
x 
f(x)
HA :

n

20
TESTING THE POPULATION MEAN WHEN
THE POPULATION VARIANCE IS KNOWN
Example 6: A random sample of 100 observations from a
normal population whose standard deviation is 50 produced a
mean of 145. Does this statistic provide sufficient evidence at
the 5% significance level to infer that population mean is less
than 150?
HO :
Rejection region:
Test statistic:
Conclusion:
x 
f(x)
HA :

n

21
READING AND EXERCISES
• Sections 10.1-10.3:
– Reading: pp. 327-343
– Exercises: 10.2,10.4,10.6
22