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### Two-tailed, Right-tailed,Left-tailed Tests

7-2 Fundamentals of Hypothesis Testing

7-3 Testing a Claim about a Mean: Large Samples

7-4 Testing a Claim about a Mean: Small Samples

7-5 Testing a Claim about a Proportion

Chapter 7Hypothesis TestingDefinition

Hypothesis

In statistics, a hypothesis is a claim or statement about a property of a population.

If, under a given assumption,

theprobability of an observed event

is exceptionally small,

we conclude that

theassumption is probably not correct.

Rare Event Rule for Inferential StatisticsThe Expected Distribution of Sample Means Assuming that = 98.6

z= - 1.96

x = 98.48

z = 1.96

x= 98.72

or

or

Figure 7-1 Central Limit TheoremLikely sample means

or

z= - 6.64

Sample data:x= 98.20

µx = 98.6

Statement about the value of a POPULATION PARAMETER

Must contain condition of EQUALITY: = ,≤ , or ≥

Test the Null Hypothesis directly

RejectH0 or fail to rejectH0

Null Hypothesis: H0 Must be true if H0 is false

Must contain condition of INEQUALITY: , < ,or >

‘Opposite’ of Null Hypothesis

Alternative Hypothesis: H1Test Statistic

- A value computed from the sample data that is used in making the decision about whether to reject the null hypothesis

- For large samples, when testing claims about population means, the test statistic is a z-score corresponding to the sample mean.

Set of all values of the test statistic that would cause a rejection of the

null hypothesis

Critical RegionCritical

Region

Set of all values of the test statistic that would cause a rejection of the

null hypothesis

Critical RegionCritical

Region

Set of all values of the test statistic that would cause a rejection of the

null hypothesis

Critical RegionCritical

Regions

denoted by

the probability that the test statistic will fall in the critical region when the null hypothesis is actually true.

common choices are 0.05, 0.01, and 0.10

Significance LevelValue or values that separate the critical region (where we reject the null hypothesis) from the values of the test statistics that do not lead to a rejection of the null hypothesisCritical Value

Reject H0

Fail to reject H0

Critical Value

( z score )

The tails in a distribution are the extreme regions bounded

by critical values.

H0: µ = 100

H1: µ 100

Two-tailed Test is divided equally between

the two tails of the critical

region

UNEQUAL means less than or greater than

Reject H0

Fail to reject H0

Reject H0

100

Values that differ significantly from 100

H0: µ 100

H1: µ > 100

Fail to reject H0

Reject H0

Right-tailed TestValues that

differ significantly

from 100

100

H0: µ 100

H1: µ < 100

Left-tailed TestReject H0

Fail to reject H0

Values that

differ significantly

from 100

100

Always test the NULL hypothesis:

Reject H0

Fail to rejectH0

Be careful to include the correct wording of the final conclusion

Conclusions in Hypothesis Testingor

Wording of Final Conclusion

Start

Only case in which original claim is rejected

Claim

contains

equality?

“There is sufficient

evidence to reject

the claim that. . .

(original claim).”

Yes

Yes

Claim becomes H0

Reject

H0?

No

“There is not

sufficient evidence

to reject the claim

that (original claim).”

No

Claim

becomes H1

Only case

in which

original

claim is

supported

“There issufficient evidence to support

the claim that . . .

(original claim).”

Yes

Reject

H0?

No

“There is not

sufficient evidence

to support the claim

that (original claim).”

Some texts use “accept the null hypothesis”

We are not proving the null hypothesis (can’t PROVE equality)

If the sample evidence is not strong enough to warrant rejection, then the null hypothesis may or may not be true (just as a defendant found NOT GUILTY may or may not be innocent)

“Fail to Reject” versus “Accept”Rejecting the null hypothesis when it is true.

(alpha) represents the probability of a type I error

Example:Rejecting a claim that the mean body temperature is 98.6 degrees when the mean really is 98.6

Type I ErrorFailing to reject the null hypothesis when it is false.

β (beta) represents the probability of a type II error

Example: Failing to reject the claim that the mean body temperature is 98.6 degrees when the mean really isn’t 98.6

Type II ErrorType I and Type II Errors

NULL HYPOTHESIS

TRUE

FALSE

Type I error

α

Rejecting a true

null hypothesis

Reject the null

hypothesis

CORRECT

DECISION

Type II error

β

Failing to reject a

false null hypothesis

Fail to reject the

null hypothesis

CORRECT

, , and nare interrelated. If one is kept constant, then an increase in one of the remaining two will cause a decrease in the other.

For any fixed , an increase in the sample size nwill cause a ??????? in

For any fixed sample size n, a decrease in will cause a ??????? in .

Conversely, an increase in will cause a ??????? in .

To decrease both and , ??????? the sample size n.

Controlling Type I and Type II Errors
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