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Tests of Hypotheses. Statistical hypothesis. Statistical hypothesis- Statement about a feature of the population (e.g. – the mean) Examples: - Mean temperature of healthy adults is 98.6°F (37°c). - A certain medication contains a mean of 245 ppm of a particular chemical.

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Statistical hypothesis l.jpg
Statistical hypothesis

Statistical hypothesis-

Statement about a feature of the population (e.g. – the mean)

Examples:

- Mean temperature of healthy adults is 98.6°F (37°c).

- A certain medication contains a mean of 245 ppm of a particular chemical.

- Mean number of people that enter a certain restaurant in a day is 125.


Example soft drink bottles l.jpg
Example – soft drink bottles

A firm that produces a certain soft drink prints on each bottle that it contains 24 oz of drink. It has been suspected that the mean amount per bottle is less than 24 oz .

In order to examine this claim, a sample of 100 bottles has been taken and the mean amount per bottle was found to be 23.4 oz.

Assume that the standard deviation of the contents of the drink in the bottles is σ=3 oz.

Is this an indication that the mean amount of drink in a bottle is less than 24 oz?


Set hypotheses l.jpg
Set hypotheses:

Two types of hypotheses:

H0 - the null hypothesis

Common beliefs, the claims that are assumed to be true up-to-date

H0 - Mean contents of soft drink bottle, μ, is 24 oz

(μ=24)

H1- the alternative hypothesis

Alternative claims that come to challenge the common beliefs

H1 – mean contents of soft drink bottle, μ, is less than 24 oz

(μ<24)



Slide6 l.jpg

Sample results:

  • If is (i) small enough

    • (ii) large enough

  • we will reject H0.

  • H0: μ=24

    H1: μ<24


    Slide7 l.jpg

    How likely are we to observe such result from a population with mean μ=24?

    The distribution of

    μ=24 oz


    Slide8 l.jpg

    We are not very likely to observe such result from a population with μ=24 (prob=0.028)


    Test statistic l.jpg
    Test statistic population with

    Z = -2

    is an example of a test statistic

    It measures the distance of the sample results from what is expected if H0 is true.


    Slide10 l.jpg

    Is the value Z=-2 unusual under H population with 0?

    0.0228

    μ=24 oz

    There is only 0.0228 chance of getting values smaller than Z=-2 if H0 is true


    P value l.jpg
    P-value population with

    The probability of getting an outcome as extreme or more extreme than the observed outcome.

    “Extreme” – far from what we would expect if H0 were true.

    The smaller the p-value, the stronger the evidence against H0.


    Level of significance l.jpg
    Level of significance population with

    α – significance level.

    It is the chance we are ready to take for rejecting H0 while in fact H0 is true

    if p-value≤ α, we say that we reject H0 at the α significance level.

    Typically, α is taken to be 0.05 or 0.01


    Slide13 l.jpg

    In the bottles example: population with

    If we required a significance level of α=0.05 then we would reject H0

    p-value=0.0228<0.05

    However, if we required a significance level of α=0.01 then we would not reject H0

    0.028

    μ=24


    Example sales of coffee l.jpg
    Example – sales of coffee population with

    Weekly sales of regular ground coffee at a supermarket have in the recent past varied according to a normal distribution with mean μ=354 units per week and standard deviation σ=33 units. The store reduces the price by 5%. Sales in the next three weeks are: 405, 378, and 411 units. Is this good evidence, at the 5% significance level, that average sales are now higher?

    Hypotheses:

    H0:

    H1:

    Sample mean:

    μ=354

    μ>354

    How far is from what we expect if H0 is true?


    Slide15 l.jpg

    μ population with =354

    Test statistic:

    P-value:

    p-value=probability of getting values that are more extreme than the test statistic if H0 is true:

    p(Z≥2.31)=1-Ф(2.31)=1-0.9896=0.0104

    Decision for α=0.05:

    P-value=0.0104< α

    We reject H0:

    There is evidence that sales have increased.


    Example systolic blood pressure l.jpg
    Example – systolic blood pressure population with

    The national center for health statistics reports that the mean systolic blood pressure for males 35 to 44 years of ages is 128 and the standard deviation in the population is 15. The medical director of a large company looks at the records of 72 executives in this age group and finds that the mean systolic blood pressure in this sample is . Is this evidence that the company’s executives have a different mean blood pressure from the general population?

    Hypotheses:

    H0:

    H1:

    no difference from the general population: μ=128

    μ≠128 (2 sided hypothesis)


    Slide17 l.jpg

    Test statistic population with :

    P-value:

    p-value=probability of getting values that are more extreme than the test statistic:

    p(Z≤-1.09)=0.1379

    But our H1 hypothesis is two sided – we must also consider the probability that Z≥1.09

    so p-value=2p(Z≤-1.09)=2(0.1379)=0.2758

    0.1379

    Z=-1.09

    Z=1.09


    Slide18 l.jpg

    Decision for population with α=0.05:

    P-value=0.2758>0.05

    we do not reject H0.

    Therefore there is no strong evidence that executives differ from other men in their blood pressure


    General rules for test of hypotheses about the mean l.jpg
    General rules for test of Hypotheses about the mean population with

    H0: μ=μ0 (known σ)

    Test statistic:

    H1: μ<μ0

    P-value = p(Z≤z)

    H1: μ>μ0

    P-value = p(Z≥z)

    H1: μ≠μ0

    P-value = 2p(Z≥|z|)

    Z

    Z

    |Z|


    Example obstetrics branch of medicine concerned with birth of children l.jpg
    Example – Obstetrics population with (branch of medicine concerned with birth of children)

    The mean birth weight in the US is 120 OZ.

    Suppose that in a sample of 100 full-term live-born deliveries in a hospital in a low socio-economic status area:

    Suppose also that the standard deviation of birth weight is σ=24 OZ.

    Examine whether the birth weight in low socio-economic status area is lower than the rest of the population.

    Hypotheses:

    H0:

    H1:

    Test statistic:

    μ=120

    μ<120


    Slide21 l.jpg

    P-value population with :

    Probability of getting values that are more extreme than the test statistic under H0.

    p-value=p(Z≤-2.083)=0.0188

    Decision for α=5%:

    P-value=0.0188<α

    Reject H0.

    There is evidence that mean birth-weight of babies in the low socio-economic status area is smaller than mean birth weight of other babies.

    Decision for α=1%:

    P-value=0.0188>α

    Do not reject H0.

    There is no evidence to suspect that mean birth-weight of babies in the low socio-economic status area is smaller then mean birth weight of other babies.


    Example nicotine l.jpg
    Example – Nicotine population with

    The nicotine content in cigarettes of a certain brand is normally distributed with mean ( in milligrams) μ and standard deviation σ=0.1. The brand advertises that the mean nicotine content of its cigarettes is 1.5, but measurements on a random sample of 100 cigarettes of this brand gave a mean . Is this evidence ,at the 1% significance level, that the mean nicotine content is actually higher than advertised?

    Hypotheses:

    H0:

    H1:

    Test statistic:

    μ=1.5

    μ>1.5


    Slide23 l.jpg

    P-value= population with

    P-value:

    Probability of getting values that are more extreme than the test statistic under H0.

    p(Z≥3)=1-p(Z<3)=1-0.9987=0.0013

    Decision for α=1%:

    P-value=0.0013< α

    Reject H0.

    There is evidence that the mean nicotine content is higher than advertised


    Example body temperature l.jpg
    Example – body temperature population with

    - Mean temperature of healthy adults=98.6°F (37°C)

    (found by Carl Wunderlich, German physician, 1868)

    - In 1992, a random sample of n=50 gave

    - Assume σ=0.67

    Is there evidence, at the 0.01 significance level, to suspect that the mean temperature differ from 98.6°F?

    Hypotheses:

    H0:

    H1:

    μ=98.6

    μ≠98.6


    Slide25 l.jpg

    Test statistic population with :

    P-value:

    2p(Z>|-3.9|)=2(1-Ф(3.9))=2(0.00012)=0.00024

    Decision for α=0.01:

    P-value=0.00024< α

    We reject H0:

    The is evidence to suspect that the mean temperature differ from 98.6

    -3.9

    3.9


    What is wrong with the following sets of hypotheses l.jpg
    What is wrong with the following sets of Hypotheses? population with

    H0:

    H1:

    Answer: the hypotheses should be about μ !

    H0: μ<5

    H1: μ=5

    Answer: the equal sign hypothesis should be in H0.

    H0: μ≠5

    H1: μ=5

    Answer: the equal sign hypothesis should be in H0.

    H0: μ=5

    H1: μ<5

    Answer: nothing is wrong


    Testing hypotheses using a confidence interval l.jpg
    Testing hypotheses using a confidence interval: population with

    Example:

    A certain maintenance medication is supposed to contain a mean of 245 ppm of a particular chemical. If the concentration is too low, the medication may not be effective; if it is too high, there may be serious side effects. The manufacturer takes a random sample of 25 portions and finds the mean to be 247 ppm. Assume concentrations to be normal with a standard deviation of 5 ppm. Is there evidence that concentrations differ significantly (α=5%) from the target level of 245 ppm?

    Hypotheses:

    H0: μ=245

    H1: μ≠245


    First lets examine the z test statistic l.jpg
    First, lets examine the Z test statistic: population with

    Test statistic:

    P-value:

    2P(Z>2)=2(0.0228)=0.0456

    Decision at 5% significance level:

    P-value>α reject H0

    The concentration differs from 245


    Now examine the hypotheses using a confidence interval l.jpg
    Now, examine the hypotheses using a confidence interval population with

    α =0.05  confidence level is 1- α = 95%

    95% CI:

    [245.04 , 248.96]

    We are 95% certain that the mean concentration is between 245.04 and 248.96.

    Since 245 is outside this CI - reject H0.

    The concentration differs from 245


    Examine the hypotheses using a confidence interval l.jpg
    Examine the hypotheses using a confidence interval population with

    H0: μ=μ0

    H1: μ≠μ0

    If μ0 is outside the confidence interval, then we reject the null hypothesis at the α significance level.

    Note: this method is good for testing two-sided hypotheses only

    [ confidence interval]

    μ0


    Example l.jpg
    Example population with

    Suppose a claim is made that the mean weight μ for a population of male runners is 57.5 kg. A random sample of size 24 yields . [σ is known to be 5 kg].

    Based on this, test the following hypotheses:

    H0: μ=57.5

    H1: μ≠57.5

    Answer using:

    a) A Z test statistic

    b) A confidence interval


    Slide32 l.jpg

    a) population with

    Test statistic:

    P-value:

    2P(Z>2.45)=2(1-.9929)=2(.0071)=.0142

    Decision at 5% significance level:

    P-value<α reject H0

    Conclusion:

    Mean weight differs from 57.5


    Slide33 l.jpg

    b) population with

    α =0.05  confidence level is 1- α = 95%

    95% CI:

    [58 , 62]

    57.5 is outside this CI - reject H0.

    Mean weight differs from 57.5

    question?: Would you reject H0: μ=59 vs. H1: μ≠59?

    No, because 59 is in the interval [58, 62]


    Example34 l.jpg
    Example population with

    In a certain university, the average grade in statistics courses is 80, and σ=11.

    A teacher at that university wanted to examine whether her students received higher grades than the rest of the stat classes. She took a sample of 30 students and recorded their grades

    hypotheses:

    H0:μ=80

    H1:μ>80

    data are:

    mean:

    95 100 82 76 75 83 75 96 75 98 79 80 79 75 100 91 81 78 100 72 94 80 87 100 97 91 70 89 99 54


    Slide35 l.jpg

    Test statistic: population with

    P-value:

    P(Z>2.51)=1-0.9940=0.006

    Decision at 5% significance level:

    P-value<α reject H0

    conclusion:

    The grades are higher than 80


    Slide36 l.jpg

    Testing hypotheses about the mean when population with σ is unknown


    What happens when is unknown l.jpg
    What happens when population with σ is unknown?

    We can estimate it from the sample:

    When the standard deviation is estimated from the sample, the test statistic is not Z:

    Or:


    T distribution l.jpg
    t-distribution population with

    • Symmetric around zero

    • Bell-shaped

    • Has wider tails than those of Z

    Z

    t(n-1)

    0



    Example40 l.jpg
    Example population with

    During a recent water shortage in a southern city, the water company randomly sampled residential water consumption on a daily basis. A random sample of 20 residents revealed:

    , S=24.3 gallons.

    Suppose the mean water consumption before the water shortage was 250 gallons. Test, at the 5% significance level, whether there was a decrease in the mean daily consumption.

    Hypotheses:

    H0:

    H1:

    μ=250

    μ<250


    Slide41 l.jpg

    Test statistic: population with

    P-value:

    P(t(19)<-4.84) = t – table

    =P(t(19)>4.84)

    p-value < 0.0025

    Decision for α=0.05:

    P-value<0.05  Reject the null hypothesis

    Conclusion:

    The daily consumption decreased below 250 gallons.

    -4.84


    Example42 l.jpg
    Example population with

    The mean age of all CEO’s for major corporations in the U.S was 48 years in 1991. A random sample of 25 CEO’s taken recently from major corporations showed that years, s=5 years. Assume that the age of CEO’s of major corporations have an approximate normal distribution. Would you conclude, at the 5% significance level, that the current mean age of all CEO’s of major corporations is not equal to 48?


    Slide43 l.jpg

    μ population with =48

    μ≠48

    Hypotheses:

    H0:

    H1:

    Test statistic:

    P-value:

    2P(t(24)<-2) = 2P(t(24)>2) t – table

    =2(0.025 to 0.05)=

    =0.05 to 0.1

    Decision for α=0.05:

    P-value>0.05  Do not reject the null hypothesis

    Conclusion:

    The mean age of CEO’s of major companies is not different from 48

    2

    -2


    Example44 l.jpg
    Example population with

    The police department will be eligible for a new hire if they can produce convincing evidence that their response times to non-emergency crime call average more than 15 minutes. A random sample of 41 calls averaged 17 minutes, with standard deviation 6 minutes. Carry out a test and decide if they are eligible for the new hire.


    Slide45 l.jpg

    μ population with =15

    μ>15

    Hypotheses:

    H0:

    H1:

    Test statistic:

    P-value:

    P(t(40)>2.13) =

    =( 0.01 to 0.025)=

    0.01<p-value<0.025

    Decision for α=0.05:

    P-value<0.05  Reject H0

    Conclusion:

    The mean response time to non-emergency calls is greater than 15 – they are eligible for the new hire.

    2.13


    Slide46 l.jpg

    back to question1 population with

    back to question2


    Practice the t table l.jpg
    Practice the t-table population with

    P(t(20)>1.325)=

    P(t(10)>2.870)=

    P(t(10)<-2.780)=

    P(t(10)>3.6)=

    P(t(10)<-3.6)=

    P(t(25)>2.51)=?

    P(t(9)<-2)=?

    P(t(19)<-4.84)

    0.1

    0.0083

    0.0083

    <0.0025

    <0.0025

    0.0083< P(t(25)>2.51) < 0.01

    0.025< P(t(9)<-2) < 0.05

    <0.0025


    A confidence interval to the mean when is unknown l.jpg
    A confidence interval to the mean when population with σ is unknown

    When σ is known:

    When σ is unknown:


    Testing hypotheses about the mean using confidence interval l.jpg
    Testing hypotheses about the mean using confidence interval population with

    If μ0 is outside the CI  reject H0

    Confidence interval


    T test with minitab l.jpg
    T-test with Minitab population with

    Example:

    Most people believe that the mean age at which babies start to walk is one year. A A researcher thought that the mean age is higher. She took a sample of 10 babies and documented the age (in months) at which they started to walk.

    The data are:

    Examine the researcher’s claim (α=5%).

    mean:

    SD:1.633

    12 11 13 14 15 13 12 11 16 13


    Slide51 l.jpg

    hypotheses: population with

    H0:μ=12

    H1:μ>12

    Test statistic:

    P-value:

    P(t(9)>1.94)=

    0.025<p.v<0.05

    Decision at 5% significance level:

    P-value < 0.05  reject H0

    conclusion:

    The mean age at which babies start to walk is higher than 12 months


    Flow diagram for testing hypotheses about the mean l.jpg
    Flow diagram for testing hypotheses about the mean population with

    σ known?

    No

    Yes


    Flow diagram for testing hypotheses about the mean53 l.jpg
    Flow diagram for testing hypotheses about the mean population with

    σ known?

    No

    Yes

    Is the sample VERY large?

    No

    Yes


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