week 1_hypothesis testing (wk 1)
DESCRIPTION
HYPOTESIS TESTING UPLOADTRANSCRIPT
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Hypothesis Testing
Niniet Indah Arvitrida, ST, [email protected]
SepuluhNopember Institute of TechnologyINDONESIA
2008
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A bit concept review...Statistics is ...Population is ...Sample is ...Variable is ...Parameter is ...Relation between statistics & parameters ...Difference between statistics and probabilityStatistics assumptions :
Population and its parameters are (unknown / known)Sample is used to ..... parameters
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Course 1 ObjectivesStudents able toFormulate null and alternative hypotheses Formulate null and alternative hypotheses
for applications involving a single population mean or proportion
Formulate a decision rule for testing a testing a hypothesishypothesis
Know how to use the test statistic, critical value, and p-value approaches to test the null hypothesis
Know what Type I Type I and Type II errors Type II errors are
Compute the probability of a Type II error
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Review of Estimating Review of Estimating Population ValuePopulation Value
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Point and Interval Estimates
A point estimate is a single number, a confidence interval provides additional
information about variability
Point Estimate
Lower
Confidence
Limit
Upper
Confidence
Limit
Width of confidence interval
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Point Estimates
We can estimate a Population Parameter …
with a SampleStatistic
(a Point Estimate)
Mean
Proportion pp
xμ
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Confidence Intervals
How much uncertainty is associated with a point estimate of a population parameter?
An interval estimate provides more information about a population characteristic than does a point estimate
Such interval estimates are called confidence intervals
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Estimation Process
(mean, μ, is unknown)
Population
Random Sample
Mean x = 50
Sample
I am 95% confident that μ is between 40 & 60.
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Confidence Level
Confidence Level
Confidence in which the interval will contain the unknown population parameter
A percentage (less than 100%)
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Confidence Interval for μ(σ Known)
AssumptionsPopulation standard deviation σ is knownPopulation is normally distributedIf population is not normal, use large
sample
Confidence interval estimate
n
σzx α/2
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Finding the Critical ValueConsider a 95% confidence interval:
z.025= -1.96 z.025= 1.96
.951
.0252
α .025
2
α
Point EstimateLower Confidence Limit
UpperConfidence Limit
z units:
x units: Point Estimate
0
1.96zα/2
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Margin of ErrorMargin of Error (e): the amount added and
subtracted to the point estimate to form the confidence interval
n
σzx /2
n
σze /2
Example: Margin of error for estimating μ, σ known:
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Chap 7-15
Factors Affecting Margin of Error
Data variation, σ : e as σ
Sample size, n : e as n
Level of confidence, 1 - : e if 1 -
n
σze /2
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Chap 7-16
Example
A sample of 11 circuits from a large normal population has a mean resistance of 2.20 ohms. We know from past testing that the population standard deviation is .35 ohms.
Determine a 95% confidence interval for the true mean resistance of the population.
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Chap 7-17
2.4068 ...............1.9932
.2068 2.20
)11(.35/ 1.96 2.20
n
σz x /2
ExampleA sample of 11 circuits from a large normal
population has a mean resistance of 2.20 ohms. We know from past testing that the population standard deviation is .35 ohms.
Solution:
(continued)
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Chap 7-18
Interpretation
We are 95% confident that the true mean resistance is between 1.9932 and 2.4068 ohms
Although the true mean may or may not be in this interval, 95% of intervals formed in this manner will contain the true mean
An incorrect interpretation is that there is 95% probability
that this interval contains the true population mean.
(This interval either does or does not contain the true mean, there is no probability for a single interval)
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Chap 7-19
Student’s t Distribution
t0
t (df = 5)
t (df = 13)t-distributions are bell-shaped and symmetric, but have ‘fatter’ tails than the normal
Standard Normal
(t with df = )
Note: t z as n increases
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Chap 7-20
Approximation for Large Samples
Since t approaches z as the sample size increases, an approximation is sometimes used when n 30:
n
stx /2
n
szx /2
Technically correct
Approximation for large n
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Now we talk about Now we talk about HYPOTHESISHYPOTHESIS
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What is a Hypothesis?A hypothesis is a claim
(assumption) about a population population parameter:
population meanmean
population proportionproportion
Example: The mean monthly cell phone bill of this city is = $42
Example: The proportion of adults in this city with cell phones is p = .68
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The Null Hypothesis, H0
States the assumptionassumption (numerical) to be tested
Example: The average number of TV sets in U.S. Homes is at least three ( )
Is alwaysalways about a populationpopulation
parameter, not about a sample statistic
3μ:H0
3μ:H0 3x:H0
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The Null Hypothesis, H0
Begin Begin with the assumption that the null hypothesis is truenull hypothesis is trueSimilar to the notion of innocent until proven guilty
Always contains “=”“=” , “≤”“≤” or ““” ” sign
May oror may not be rejected
(continued)
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The Alternative Hypothesis, HA
Is the opposite of the null hypothesise.g.: The average number of TV sets in
U.S. homes is less than 3 ( HA: < 3 )
NeverNever contains the “=” , “≤” or “” sign
May or may not be acceptedIs generally the hypothesis that is
believed (or needs to be supported) by the researcher
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Population
Claim: thepopulationmean age is 50.(Null Hypothesis:
REJECT
Supposethe samplemean age is 20: x = 20
SampleNull Hypothesis
20 likely if = 50?Is
Hypothesis Testing Process
If not likely,
Now select a random sample
H0: = 50 )
x
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Reason for Rejecting H0
Sampling Distribution of x
= 50If H0 is true
If it is unlikely that we would get a sample mean of this value ...
... then we reject the null
hypothesis that = 50.
20
... if in fact this were the population mean…
x
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Level of Significance, Defines unlikely values of sample statistic
if null hypothesis is true
Defines rejection region rejection region of the sampling distribution
Is designated by , (level of significance)
Typical values are .01, .05, or .10Is selected by the researcher at the beginning
Provides the critical value(s) of the test
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Level of Significance and the Rejection Region
H0: μ ≥ 3
HA: μ < 3 0
H0: μ ≤ 3
HA: μ > 3
H0: μ = 3
HA: μ ≠ 3
/2
Represents critical value
Lower tail test
Level of significance =
0
0
/2
Upper tail test
Two tailed test
Rejection region is shaded
1-α
1-α
1-α
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Errors in Making DecisionsType I Error
Reject a true null hypothesisConsidered a serious type of error
The probability of Type I Error is Called level of significance level of significance of the
testSet by researcher in advance
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Errors in Making Decisions
Type II ErrorFail to reject a false null hypothesis
The probability of Type II Error is β
(continued)
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Outcomes and Probabilities
State of Nature
Decision
Do NotReject
H0
No error (1 - )
Type II Error ( β )
RejectH0
Type I Error( )
Possible Hypothesis Test Outcomes
H0 False H0 True
Key:Outcome
(Probability) No Error ( 1 - β )
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Type I & II Error Relationship
Type I and Type II errors can not happen at the same time
Type I error can only occur if H0 is true
Type II error can only occur if H0 is false
If Type I error probability ( ) , then
Type II error probability ( β )
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Factors Affecting Type II Error
All else equal, β when the difference between
hypothesized parameter and its true value
β when
β when σ
β when n
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Critical Value Approach to Testing
Convert sample statistic (e.g.: ) to test statistic ( Z or t statistic )
Determine the critical value(s) for a specifiedlevel of significance from a table or computer
If the test statistic falls in the rejection
region, reject H0 ; otherwise do not reject H0
x
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Lower Tail Tests
Reject H0 Do not reject H0
The cutoff value,
or , is called a
critical value
-zα
xα
-zα xα
0
μ
H0: μ ≥ 3
HA: μ < 3
n
σzμx
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Upper Tail Tests
Reject H0Do not reject H0
The cutoff value,
or , is called a
critical value
zα
xα
zα xα
0μ
H0: μ ≤ 3
HA: μ > 3
n
σzμx
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Two Tailed Tests
Do not reject H0 Reject H0Reject H0
There are two cutoff
values (critical values):
or /2
-zα/2
xα/2
± zα/2
xα/2
0μ0
H0: μ = 3
HA: μ
3
zα/2
xα/2
n
σzμx /2/2
Lower
Upperxα/2
Lower Upper
/2
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Critical Value Approach to Testing
Convert sample statistic ( ) to a test statistic ( Z or t statistic )
x
Known
Large Samples
Unknown
Hypothesis Tests for
Small Samples
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Calculating the Test Statistic
Known
Large Samples
Unknown
Hypothesis Tests for μ
Small Samples
The test statistic is:
n
σμx
z
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Calculating the Test Statistic
Known
Large Samples
Unknown
Hypothesis Tests for
Small Samples
The test statistic is:
n
sμx
t 1n
But is sometimes approximated using a z:
n
σμx
z
(continued)
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Calculating the Test Statistic
Known
Large Samples
Unknown
Hypothesis Tests for
Small Samples
The test statistic is:
n
sμx
t 1n
(The population must be approximately normal)
(continued)
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Review: Steps in Hypothesis Testing
1. Specify the population value of interest
2. Formulate the appropriate null and alternative hypotheses
3. Specify the desired level of significance
4. Determine the rejection region
5. Obtain sample evidence and compute the test statistic
6. Reach a decision and interpret the result
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Hypothesis Testing Example
Test the claim that the true mean # of TV sets in US homes is at least 3.
(Assume σ = 0.8)
1. Specify the population value of interest The mean number of TVs in US homes
2. Formulate the appropriate null and alternative hypotheses H0: μ 3 HA: μ < 3 (This is a lower tail test)
3. Specify the desired level of significance Suppose that = .05 is chosen for this test
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Hypothesis Testing Example4. Determine the rejection region
Reject H0 Do not reject H0
= .05
-zα= -1.645 0
This is a one-tailed test with = .05. Since σ is known, the cutoff value is a z value:
Reject H0 if z < z = -1.645 ; otherwise do not reject H0
(continued)
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Hypothesis Testing Example5. Obtain sample evidence and compute the
test statistic
Suppose a sample is taken with the following results: n = 100, x = 2.84 ( = 0.8 is assumed known)
Then the test statistic is:
2.0.08
.16
100
0.832.84
n
σμx
z
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Hypothesis Testing Example 6. Reach a decision and interpret the result
Reject H0 Do not reject H0
= .05
-1.645 0
-2.0
Since z = -2.0 < -1.645, we reject the null hypothesis that the mean number of TVs in US homes is at least 3
(continued)
z
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Hypothesis Testing Example An alternate way of constructing rejection region:
Reject H0
= .05
2.8684
Do not reject H0
3
2.84
Since x = 2.84 < 2.8684, we reject the null hypothesis
(continued)
x
Now expressed in x, not z units
2.8684100
0.81.6453
n
σzμx αα
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p-Value Approach to Testing
Convert Sample Statistic (e.g. ) to Test Statistic ( Z or t statistic )
Obtain the p-value from a table or computer
Compare the p-value with
If p-value < , reject H0
If p-value , do not reject H0
x
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p-Value Approach to Testing
p-value: Probability of obtaining a test statistic more extreme ( ≤ or ) than the observed sample value given H0 is true
Also called observed level of significance
Smallest value of for which H0 can be
rejected
(continued)
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p-value exampleExample: How likely is it to see a sample
mean of 2.84 (or something further below the mean) if the true mean is = 3.0?
p-value =.0228
= .05
2.8684 3
2.84
x
.02282.0)P(z
1000.8
3.02.84zP
3.0)μ|2.84xP(
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p-value exampleCompare the p-value with
If p-value < , reject H0
If p-value , do not reject H0
Here: p-value = .0228 = .05
Since .0228 < .05, we reject the null hypothesis
(continued)
p-value =.0228
= .05
2.8684 3
2.84
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Example: Upper Tail z Test for Mean ( Known)
A phone industry manager thinks that customer monthly cell phone bill have increased, and now average over $52 per month. The company wishes to test this claim. (Assume = 10 is known)
H0: μ ≤ 52 the average is not over $52 per month
HA: μ > 52 the average is greater than $52 per month(i.e., sufficient evidence exists to support the manager’s claim)
Form hypothesis test:
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Suppose that = .10 is chosen for this test
Find the rejection region:
Reject H0Do not reject H0
= .10
zα=1.280
Reject H0
Reject H0 if z > 1.28
Example: Find Rejection Region
(continued)
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Review:Finding Critical Value - One Tail
Z .07 .09
1.1 .3790 .3810 .3830
1.2 .3980 .4015
1.3 .4147 .4162 .4177z 0 1.28
.08
Standard Normal Distribution Table (Portion)What is z given =
0.10?
= .10
Critical Value = 1.28
.90
.3997
.10
.40.50
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Example: Test Statistic
0.88
64
105253.1
n
σμx
z
(continued)
Obtain sample evidence and compute the test statistic
Suppose a sample is taken with the following results:
n = 64, x = 53.1 (=10 was assumed known)
Then the test statistic is:
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Example: DecisionReach a decision and interpret the result:
Reject H0Do not reject H0
= .10
1.280
Reject H0
Do not reject H0 since z = 0.88 ≤ 1.28
i.e.: there is not sufficient evidence that the mean bill is over $52
z = .88
(continued)
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p -Value SolutionCalculate the p-value and compare to
Reject H0
= .10
Do not reject H0 1.28
0
Reject H0
z = .88
(continued)
.1894
.3106.50.88)P(z
6410
52.053.1zP
52.0)μ|53.1xP(
p-value = .1894
Do not reject H0 since p-value = .1894 > = .10
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Example: Two-Tail Test( Unknown)
The average cost of a hotel room in New York is said to be $168 per night. A random sample of 25 hotels resulted in x = $172.50 and
s = $15.40. Test at the = 0.05 level.
(Assume the population distribution is normal)
H0: μ= 168
HA: μ
168
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Example Solution: Two-Tail Test
= 0.05
n = 25
is unknown, so use a t statistic
Critical Value:
t24 = ± 2.0639 Do not reject H0: not sufficient evidence that true mean cost is different than $168
Reject H0Reject H0
/2=.025
-tα/2
Do not reject H0
0 tα/2
/2=.025
-2.0639 2.0639
1.46
25
15.40168172.50
n
sμx
t 1n
1.46
H0: μ= 168
HA: μ
168
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Hypothesis Tests for Proportions
Involves categorical values
Two possible outcomes
“Success” (possesses a certain characteristic)
“Failure” (does not possesses that characteristic)
Fraction or proportion of population in the “success” category is denoted by p
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ProportionsSample proportion in the success category is
denoted by p
When both np and n(1-p) are at least 5, p can be approximated by a normal distribution with mean and standard deviation
(continued)
sizesample
sampleinsuccessesofnumber
n
xp
pμP
n
p)p(1σ
p
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Hypothesis Tests for Proportions
The sampling distribution of p is normal, so the test statistic is a z value:
n)p(p
ppz
1
np 5and
n(1-p) 5
Hypothesis Tests for p
np < 5or
n(1-p) < 5
Not discussed in this chapter
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Example: z Test for Proportion
A marketing company claims that it receives 8% responses from its mailing. To test this claim, a random sample of 500 were surveyed with 25 responses. Test at the = .05 significance level.
Check:
n p = (500)(.08) = 40
n(1-p) = (500)(.92) = 460
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Z Test for Proportion: Solution
= .05
n = 500, p = .05
Reject H0 at = .05
H0: p = .08
HA: p
.08
Critical Values: ± 1.96
Test Statistic:
Decision:
Conclusion:
z0
Reject Reject
.025.025
1.96
-2.47
There is sufficient evidence to reject the company’s claim of 8% response rate.
2.47
500.08).08(1
.08.05
np)p(1
ppz
-1.96
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p -Value SolutionCalculate the p-value and compare to (For a two sided test the p-value is always two sided)
Do not reject H0
Reject H0Reject H0
/2 = .025
1.960
z = -2.47
(continued)
0.01362(.0068)
.4932)2(.5
2.47)P(x2.47)P(z
p-value = .0136:
Reject H0 since p-value = .0136 < = .05
z = 2.47
-1.96
/2 = .025
.0068.0068
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Type II ErrorType II error is the probability of failing to reject a false H0
Reject H0: μ 52
Do not reject H0 : μ 52
5250
Suppose we fail to reject H0: μ 52 when in fact the true mean is μ = 50
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Type II ErrorSuppose we do not reject H0: 52 when
in fact the true mean is = 50
Reject H0: 52
Do not reject H0 : 52
5250
This is the true distribution of x if = 50
This is the range of x where H0 is not rejected
(continued)
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Type II ErrorSuppose we do not reject H0: μ 52
when in fact the true mean is μ = 50
Reject H0: μ 52
Do not reject H0 : μ 52
5250
β
Here, β = P( x cutoff ) if μ = 50
(continued)
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Calculating βSuppose n = 64 , σ = 6 , and = .05
Reject H0: μ 52
Do not reject H0 : μ 52
5250
So β = P( x 50.766 ) if μ = 50
50.76664
61.64552
n
σzμxcutoff
(for H0 : μ 52)
50.766
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Calculating βSuppose n = 64 , σ = 6 , and = .05
Reject H0: μ 52
Do not reject H0 : μ 52
.1539.3461.51.02)P(z
646
5050.766zP50)μ|50.766xP(
5250
(continued)
Probability of type II error:
β = .1539
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Chapter SummaryAddressed hypothesis testing
methodology
Performed z Test for the mean (σ known)
Discussed p–value approach to hypothesis testing
Performed one-tail and two-tail tests . . .
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Chapter Summary
Performed t test for the mean (σ unknown)
Performed z test for the proportion
Discussed type II error and computed its probability
(continued)