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# tests of hypotheses - PowerPoint PPT Presentation

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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### Tests of Hypotheses

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.

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?

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)

• If is (i) small enough

• (ii) large enough

• we will reject H0.

• H0: μ=24

H1: μ<24

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

The distribution of

μ=24 oz

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

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.

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 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 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

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 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?

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 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)

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

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 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 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

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 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

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 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

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? population with

H0:

H1:

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

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: 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 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 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 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

a) A Z test statistic

b) A confidence interval

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

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]

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

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

Testing hypotheses about the mean when population with σ is unknown

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 population with

• Symmetric around zero

• Bell-shaped

• Has wider tails than those of Z

Z

t(n-1)

0

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

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

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?

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

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.

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

back to question1 population with

back to question2

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 population with σ is unknown

When σ is known:

When σ is unknown:

Testing hypotheses about the mean using confidence interval population with

If μ0 is outside the CI  reject H0

Confidence interval

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

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 population with

σ known?

No

Yes

Flow diagram for testing hypotheses about the mean population with

σ known?

No

Yes

Is the sample VERY large?

No

Yes