Transcript null hypothesis - RIT

Hypothesis Testing Basics
© 2010 Pearson Prentice Hall. All rights reserved
A hypothesis is a statement regarding a
characteristic of one or more
populations.
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The null hypothesis, denoted H0, is a
statement to be tested. The null
hypothesis is a statement of no change,
no effect or no difference. The null
hypothesis is assumed true until
evidence indicates otherwise.
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The alternative hypothesis, denoted H1, is
a statement that is to the contrary of the
null hypothesis. In a standard hypothesis
testing process this is the statement we
are trying to find evidence to support.
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There are three basic ways to set up the null and
alternative hypotheses:
1. Equal versus not equal hypothesis (two-tailed test)
H0: parameter = some value
H1: parameter ≠ some value
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There are three basic ways to set up the null and
alternative hypotheses:
1. Equal versus not equal hypothesis (two-tailed test)
H0: parameter = some value
H1: parameter ≠ some value
2. Equal versus less than (left-tailed test)
H0: parameter = some value
H1: parameter < some value
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There are three basic ways to set up the null and
alternative hypotheses:
1. Equal versus not equal hypothesis (two-tailed test)
H0: parameter = some value
H1: parameter ≠ some value
2. Equal versus less than (left-tailed test)
H0: parameter = some value
H1: parameter < some value
3. Equal versus greater than (right-tailed test)
H0: parameter = some value
H1: parameter > some value
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“In Other Words”
The null hypothesis is a statement of “status
quo” or “no difference” and always contains
a statement of equality. The null hypothesis
is assumed to be true until we have evidence
to the contrary. The claim that we are trying
to gather evidence for determines the
alternative hypothesis.
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Parallel Example : Forming Hypotheses
For each of the following claims, determine the null and alternative
hypotheses. State whether the test is two-tailed, left-tailed or
right-tailed.
a)
In 2008, 62% of American adults regularly volunteered their
time for charity work. A researcher believes that this
percentage is different today.
b)
According to a study published in March, 2006 the mean
length of a phone call on a cellular telephone was 3.25
minutes. A researcher believes that the mean length of a call
has increased since then.
c)
Using an old manufacturing process, the standard deviation of
the amount of wine put in a bottle was 0.23 ounces. With new
equipment, the quality control manager believes the standard
deviation has decreased.
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Solution
a)
In 2008, 62% of American adults regularly volunteered
their time for charity work. A researcher believes that this
percentage is different today.
The hypothesis deals with a population proportion, p. If
the percentage participating in charity work is no different
than in 2008, it will be 0.62 so the null hypothesis is H0:
p=0.62.
Since the researcher believes that the percentage is
different today, the alternative hypothesis is a two-tailed
hypothesis: H1: p≠0.62.
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Solution
b)
According to a study published in March, 2006 the mean
length of a phone call on a cellular telephone was 3.25
minutes. A researcher believes that the mean length of a
call has increased since then.
The hypothesis deals with a population mean, . If the
mean call length on a cellular phone is no different than in
2006, it will be 3.25 minutes so the null hypothesis is H0:
=3.25.
Since the researcher believes that the mean call length
has increased, the alternative hypothesis is: H1:  > 3.25,
a right-tailed test.
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Four Outcomes from Hypothesis Testing
1.
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We reject the null hypothesis when the alternative hypothesis
is true. This decision would be correct.
Four Outcomes from Hypothesis Testing
1.
We reject the null hypothesis when the alternative hypothesis
is true. This decision would be correct.
2.
We do not reject the null hypothesis when the null hypothesis
is true. This decision would be correct.
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Four Outcomes from Hypothesis Testing
1.
We reject the null hypothesis when the alternative hypothesis
is true. This decision would be correct.
2.
We do not reject the null hypothesis when the null hypothesis
is true. This decision would be correct.
3.
We reject the null hypothesis when the null hypothesis is true.
This decision would be incorrect. This type of error is called a
Type I error.
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Four Outcomes from Hypothesis Testing
1.
We reject the null hypothesis when the alternative hypothesis
is true. This decision would be correct.
2.
We do not reject the null hypothesis when the null hypothesis
is true. This decision would be correct.
3.
We reject the null hypothesis when the null hypothesis is true.
This decision would be incorrect. This type of error is called a
Type I error.
4.
We do not reject the null hypothesis when the alternative
hypothesis is true. This decision would be incorrect. This type
of error is called a Type II error.
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Hypothesis Testing Error Table
We reject H0
H0 is true
H0 is false
Type I error
No error committed
committed,
We Fail to reject H0
α
No error committed Type II error
committed,
β
The probability of making a Type I error, ,
is chosen by the researcher before the
sample data is collected.
The level of significance, , is the
probability of making a Type I error.
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As the probability of a Type I error
increases, the probability of a Type II
error decreases, and vice-versa.
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CAUTION!
We never “accept” the null hypothesis
because without having access to the entire
population, we don’t know the exact value of
the parameter stated in the null. Rather, we
say that we do not reject the null hypothesis.
This is just like the court system. We never
declare a defendant “innocent”, but rather say
the defendant is “not guilty”.
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