Hypothesis tests | Bài giảng số 9 chương 5 học phần Applied statistics | Trường Đại học Quốc tế, Đại học Quốc gia Thành phố Hồ Chí Minh

Of course, not all hypothesis tests involve research hypotheses. First, we consider applications of hypothesis testing where we begin with a belief or an assumption that a statement about the value of a population parameter is true. We will then use a hypothesis test to challenge the assumption and determine if there is statistical evidence to conclude that the assumption is incorrect. In these situations, it is helpful to develop the null hypothesis first. The null hypothesis H0 expresses the belief or assumption about the value of the population parameter. Tài liệu giúp bạn tham khảo, ôn tập và đạt kết quả cao. Mời bạn đón xem.

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Hypothesis tests | Bài giảng số 9 chương 5 học phần Applied statistics | Trường Đại học Quốc tế, Đại học Quốc gia Thành phố Hồ Chí Minh

Of course, not all hypothesis tests involve research hypotheses. First, we consider applications of hypothesis testing where we begin with a belief or an assumption that a statement about the value of a population parameter is true. We will then use a hypothesis test to challenge the assumption and determine if there is statistical evidence to conclude that the assumption is incorrect. In these situations, it is helpful to develop the null hypothesis first. The null hypothesis H0 expresses the belief or assumption about the value of the population parameter. Tài liệu giúp bạn tham khảo, ôn tập và đạt kết quả cao. Mời bạn đón xem.

26 13 lượt tải Tải xuống
APPLIED STATISTICS
COURSE CODE: ENEE1006IU
Lecture 9:
Chapter 5: Hypothesis tests
(3 credits: 2 is for lecture, 1 is for lab-work)
Instructor: TRAN THANH TU Email:
tu@hcmiu.edu.vn
tu@hcmiu.edu.vn 1
tu@hcmiu.edu.vn 2
5.1. HYPOTHESIS TESTING AND DECISION MAKING
•Developing null and alternave hypotheses
•Type I and type II errors
•Populaon mean: σ known
•Populaon mean: σ unknown
•Populaon proporon
•Hypothesis tesng and decision making
•Calculang the probability of type II errors
tu@hcmiu.edu.vn 3
•Determining the sample size for a hypothesis test about a
populaon mean
5.1. HYPOTHESIS TESTING AND DECISION MAKING
A hypothesis is a statement or asseron about the state of nature
(about the true value of an unknown populaon parameter):
The accused is innocent
=100
Every hypothesis implies its contradicon or alternave:
The accused is guilty
tu@hcmiu.edu.vn 4
100
A hypothesis is either true or false, and you may fail to reject it or you
may reject it on the basis of informaon:
Trial tesmony and evidence
Sample data
5.1. HYPOTHESIS TESTING AND DECISION MAKING
•Developing null and alternave hypotheses:
In hypothesis tesng we begin by making a tentave assumpon about a populaon
parameter. This tentave assumpon is called the null hypothesis (H
0
)
tu@hcmiu.edu.vn 5
We then dene another hypothesis, called the alternave hypothesis (H
a
), which is
the opposite of what is stated in the null hypothesis.
The hypothesis tesng procedure uses data from a sample to test the two compeng
statements indicated by H
0
and H
a
.
Care must be taken to •H
0
: µ =100 ; H
0
: µ > 100 structure the hypotheses H
a
: µ ≠ 100; H
a
: µ 100
appropriately so that the
hypothesis tesng conclusion provides the informaon the researcher or decision • H
0
and H
a
are: maker wants.– Mutually exclusive Only one can be true.
– Exhausve
Together they cover all possibilies, so one or the other must be true.
5.1. HYPOTHESIS TESTING AND DECISION MAKING
tu@hcmiu.edu.vn 6
•Developing null and alternave hypotheses:
The Alternave Hypothesis as a Research Hypothesis:
Before adopng something new, it is desirable to conduct research to
determine if there is stascal support for the conclusion that the new
approach is indeed beer.
In such cases, the research hypothesis is stated as the alternave hypothesis.
The alternave hypothesis is that the new method is beer.
The null hypothesis is that the new method is no beer than the old method.
5.1. HYPOTHESIS TESTING AND DECISION MAKING •Developing
null and alternave hypotheses:
The Null Hypothesis as an Assumpon to Be Challenged:
Of course, not all hypothesis tests involve research hypotheses.
tu@hcmiu.edu.vn 7
First, we consider applicaons of hypothesis tesng where we begin with a
belief or an assumpon that a statement about the value of a populaon
parameter is true.
We will then use a hypothesis test to challenge the assumpon and determine
if there is stascal evidence to conclude that the assumpon is incorrect.
In these situaons, it is helpful to develop the null hypothesis rst.
The null hypothesis H
0
expresses the belief or assumpon about the value of
the populaon parameter.
The alternave hypothesis H
a
is that the belief or assumpon is incorrect.
5.1. HYPOTHESIS TESTING AND DECISION MAKING
•Developing null and alternave hypotheses:
Summary of Forms for Null and Alternave Hypotheses:
tu@hcmiu.edu.vn 8
•The hypothesis tests in this chapter involve two populaon parameters: the
populaon mean and the populaon proporon.
•Depending on the situaon, hypothesis tests about a populaon parameter may
take one of three forms: two use inequalies in the null hypothesis; the third uses
an equality in the null hypothesis.
•For hypothesis tests involving a populaon mean, we let µ
0
denote the
hypothesized value and we must choose one of the following three forms for the
hypothesis test.
Two-
One-tailed
tailed test test
5.1. HYPOTHESIS
TESTING AND DECISION MAKING
tu@hcmiu.edu.vn 9
•Type I and type II errors:
•The null and alternave hypotheses are compeng statements about the
populaon.
•Either the null hypothesis H
0
is true or the alternave hypothesis H
a
is true, but not
both.
•Ideally the hypothesis tesng procedure should lead to the acceptance of H
0
when
H
0
is true and the rejecon of H
0
when H
a
is true.
•Unfortunately, the correct conclusions are not always possible.
•Because hypothesis tests are based on sample informaon, we must allow for the
possibility of errors.
5.1. HYPOTHESIS TESTING AND DECISION MAKING
tu@hcmiu.edu.vn 10
•Type I and type II errors:
By selecng α, a person is
controlling the probability
of making a type I error:
- If the cost of making a
type I error is high,
small values of α are
preferred.
- If the cost of making a
type I error is not too
high, larger values of α
are typically used.
tu@hcmiu.edu.vn 11
The probability of making a type I error when the null hypothesis is true as an
equality is called the level of signicance.
Applicaons of hypothesis tesng that only control for the type I error are called
signicance tests.
5.1. HYPOTHESIS TESTING AND DECISION MAKING
•Populaon mean: σ-known:
•σ-known case: applicaons in which historical data and/or other informaon are
available that enable us to obtain a good esmate of the populaon standard
deviaon prior to sampling.
tu@hcmiu.edu.vn 12
One-Tailed Test: One-tailed tests about a populaon mean take one of the
following two forms:
For hypothesis tests about a populaon mean in the σ-known case, we use the
standard normal random variable z as a test stasc to determine whether
deviates from the hypothesized value of µ enough to jusfy rejecng the null
hypothesis.
5.1. HYPOTHESIS TESTING AND DECISION MAKING
•Populaon mean: σ-known:
tu@hcmiu.edu.vn 13
Test stasc z:
p-value approach: the p-value approach uses the value of the test stasc z
to compute a probability called a p-value.
A p-value is a probability that provides a measure of the evidence against
the null hypothesis provided by the sample.
Smaller p-values indicate more evidence against H
0
.
The p-value is used to determine whether the null hypothesis should be
rejected.
(α: level of signicant, common choices for α are 0.05 and 0.01)
5.1. HYPOTHESIS TESTING AND DECISION MAKING
tu@hcmiu.edu.vn 14
•Populaon mean: σ-known:
Test stasc z:
Crical value approach: the crical value approach requires that we rst
determine a value for the test stasc called the crical value.
For a lower tail test, the crical value serves as a benchmark for
determining whether the value of the test stasc z is small enough to
reject the null hypothesis.
It is the value of the test stasc that corresponds to an area of α (the level
of signicance) in the lower tail of the sampling distribuon of the test
stasc.
In other words, the crical value is the largest value of the test stasc z
that will result in the rejecon of the null hypothesis.
tu@hcmiu.edu.vn 15
5.1. HYPOTHESIS TESTING AND DECISION MAKING
•Populaon mean: σ-known:
Test stasc z:
For an upper tail test, using the crical value approach causes us to reject the
null hypothesis if the value of the test stasc is greater than
tu@hcmiu.edu.vn 16
5.1. HYPOTHESIS TESTING AND DECISION MAKING
•Populaon mean: σ-known:
tu@hcmiu.edu.vn 17
Two-Tailed Test:
p-value approach:
Values of the test stasc in either tail provide evidence against the null
hypothesis.
The p-value is the probability of obtaining a value for the test stasc as
unlikely as or more unlikely than that provided by the sample.
The computaon of the p-value for a two-tailed test may seem a bit
confusing as compared to the computaon of the p-value for a onetailed test.
Crical value approach:
Crical values for the test will occur in both the lower and upper tails of the
standard normal distribuon.
5.1. HYPOTHESIS TESTING AND DECISION MAKING
tu@hcmiu.edu.vn 18
•Populaon mean: σ-known: Summary
tu@hcmiu.edu.vn 19
5.1. HYPOTHESIS TESTING AND DECISION MAKING
tu@hcmiu.edu.vn 20
5.1. HYPOTHESIS TESTING AND DECISION MAKING
| 1/38

Preview text:

APPLIED STATISTICS COURSE CODE: ENEE1006IU Lecture 9: Chapter 5: Hypothesis tests
(3 credits: 2 is for lecture, 1 is for lab-work)
Instructor: TRAN THANH TU Email: tttu@hcmiu.edu.vn tttu@hcmiu.edu.vn 1
5.1. HYPOTHESIS TESTING AND DECISION MAKING
•Developing null and alternative hypotheses •Type I and type II errors •Population mean: σ known
•Population mean: σ unknown •Population proportion
•Hypothesis testing and decision making
•Calculating the probability of type II errors tttu@hcmiu.edu.vn 2
•Determining the sample size for a hypothesis test about a population mean
5.1. HYPOTHESIS TESTING AND DECISION MAKING
•A hypothesis is a statement or assertion about the state of nature
(about the true value of an unknown population parameter): The accused is innocent =100
•Every hypothesis implies its contradiction or alternative: The accused is guilty tttu@hcmiu.edu.vn 3 100
•A hypothesis is either true or false, and you may fail to reject it or you
may reject it on the basis of information: Trial testimony and evidence Sample data
5.1. HYPOTHESIS TESTING AND DECISION MAKING
•Developing null and alternative hypotheses:
In hypothesis testing we begin by making a tentative assumption about a population
parameter. This tentative assumption is called the null hypothesis (H0) tttu@hcmiu.edu.vn 4
We then define another hypothesis, called the alternative hypothesis (Ha), which is
the opposite of what is stated in the null hypothesis.
The hypothesis testing procedure uses data from a sample to test the two competing
statements indicated by H0 and Ha.
Care must be taken to •H0: µ =100 ; H0: µ > 100 structure the hypotheses Ha: µ ≠ 100; Ha: µ 100 appropriately so that the
hypothesis testing conclusion provides the information the researcher or decision • H0
and Ha are: maker wants.– Mutually exclusive Only one can be true. – Exhaustive
Together they cover all possibilities, so one or the other must be true.
5.1. HYPOTHESIS TESTING AND DECISION MAKING tttu@hcmiu.edu.vn 5
•Developing null and alternative hypotheses:
The Alternative Hypothesis as a Research Hypothesis:
Before adopting something new, it is desirable to conduct research to
determine if there is statistical support for the conclusion that the new approach is indeed better.
In such cases, the research hypothesis is stated as the alternative hypothesis.
The alternative hypothesis is that the new method is better.
The null hypothesis is that the new method is no better than the old method.
5.1. HYPOTHESIS TESTING AND DECISION MAKING •Developing
null and alternative hypotheses:
The Null Hypothesis as an Assumption to Be Challenged:
Of course, not all hypothesis tests involve research hypotheses. tttu@hcmiu.edu.vn 6
First, we consider applications of hypothesis testing where we begin with a
belief or an assumption that a statement about the value of a population parameter is true.
We will then use a hypothesis test to challenge the assumption and determine
if there is statistical evidence to conclude that the assumption is incorrect.
In these situations, it is helpful to develop the null hypothesis first.
The null hypothesis H0 expresses the belief or assumption about the value of the population parameter.
The alternative hypothesis Ha is that the belief or assumption is incorrect.
5.1. HYPOTHESIS TESTING AND DECISION MAKING
•Developing null and alternative hypotheses:
Summary of Forms for Null and Alternative Hypotheses: tttu@hcmiu.edu.vn 7
•The hypothesis tests in this chapter involve two population parameters: the
population mean and the population proportion.
•Depending on the situation, hypothesis tests about a population parameter may
take one of three forms: two use inequalities in the null hypothesis; the third uses
an equality in the null hypothesis.
•For hypothesis tests involving a population mean, we let µ0 denote the
hypothesized value and we must choose one of the following three forms for the hypothesis test. Two- One-tailed tailed test test 5.1. HYPOTHESIS TESTING AND DECISION MAKING tttu@hcmiu.edu.vn 8 •Type I and type II errors:
•The null and alternative hypotheses are competing statements about the population.
•Either the null hypothesis H0 is true or the alternative hypothesis Ha is true, but not both.
•Ideally the hypothesis testing procedure should lead to the acceptance of H0 when
H0 is true and the rejection of H0 when Ha is true.
•Unfortunately, the correct conclusions are not always possible.
•Because hypothesis tests are based on sample information, we must allow for the possibility of errors.
5.1. HYPOTHESIS TESTING AND DECISION MAKING tttu@hcmiu.edu.vn 9 •Type I and type II errors: By selecting α, a person is controlling the probability of making a type I error: - If the cost of making a type I error is high, small values of α are preferred. - If the cost of making a type I error is not too high, larger values of α are typically used. tttu@hcmiu.edu.vn 10
The probability of making a type I error when the null hypothesis is true as an
equality is called the level of significance.
Applications of hypothesis testing that only control for the type I error are called significance tests.
5.1. HYPOTHESIS TESTING AND DECISION MAKING •Population mean: σ-known:
•σ-known case: applications in which historical data and/or other information are
available that enable us to obtain a good estimate of the population standard deviation prior to sampling. tttu@hcmiu.edu.vn 11
One-Tailed Test: One-tailed tests about a population mean take one of the following two forms:
For hypothesis tests about a population mean in the σ-known case, we use the
standard normal random variable z as a test statistic to determine whether
deviates from the hypothesized value of µ enough to justify rejecting the null hypothesis.
5.1. HYPOTHESIS TESTING AND DECISION MAKING •Population mean: σ-known: tttu@hcmiu.edu.vn 12 Test statistic z:
p-value approach: the p-value approach uses the value of the test statistic z
to compute a probability called a p-value.
A p-value is a probability that provides a measure of the evidence against
the null hypothesis provided by the sample.
Smaller p-values indicate more evidence against H0.
The p-value is used to determine whether the null hypothesis should be rejected.
(α: level of significant, common choices for α are 0.05 and 0.01)
5.1. HYPOTHESIS TESTING AND DECISION MAKING tttu@hcmiu.edu.vn 13 •Population mean: σ-known: Test statistic z:
Critical value approach: the critical value approach requires that we first
determine a value for the test statistic called the critical value.
For a lower tail test, the critical value serves as a benchmark for
determining whether the value of the test statistic z is small enough to reject the null hypothesis.
It is the value of the test statistic that corresponds to an area of α (the level
of significance) in the lower tail of the sampling distribution of the test statistic.
In other words, the critical value is the largest value of the test statistic z
that will result in the rejection of the null hypothesis. tttu@hcmiu.edu.vn 14
5.1. HYPOTHESIS TESTING AND DECISION MAKING •Population mean: σ-known: Test statistic z:
For an upper tail test, using the critical value approach causes us to reject the
null hypothesis if the value of the test statistic is greater than tttu@hcmiu.edu.vn 15
5.1. HYPOTHESIS TESTING AND DECISION MAKING •Population mean: σ-known: tttu@hcmiu.edu.vn 16 Two-Tailed Test: p-value approach:
Values of the test statistic in either tail provide evidence against the null hypothesis.
The p-value is the probability of obtaining a value for the test statistic as
unlikely as or more unlikely than that provided by the sample.
The computation of the p-value for a two-tailed test may seem a bit
confusing as compared to the computation of the p-value for a onetailed test. Critical value approach:
Critical values for the test will occur in both the lower and upper tails of the standard normal distribution.
5.1. HYPOTHESIS TESTING AND DECISION MAKING tttu@hcmiu.edu.vn 17
•Population mean: σ-known: Summary tttu@hcmiu.edu.vn 18
5.1. HYPOTHESIS TESTING AND DECISION MAKING tttu@hcmiu.edu.vn 19
5.1. HYPOTHESIS TESTING AND DECISION MAKING tttu@hcmiu.edu.vn 20