This document provides an overview of hypothesis testing, including:
- The two types of statistical hypotheses: the null hypothesis (H0) and alternative hypothesis (H1)
- How to define the hypotheses for different study designs, such as one-tailed, two-tailed, or directional alternatives
- How to determine critical values and critical regions based on the significance level (α)
- How to calculate a test statistic and make a decision to reject or not reject the null hypothesis
- Examples of hypothesis testing for different research scenarios involving means
Day-2_Presentation for SPSS parametric workshop.pptxrjaisankar
This document provides an overview of parametric statistical tests, including t-tests and analysis of variance (ANOVA). It discusses the concepts of statistical inference, hypothesis testing, null and alternative hypotheses, types of errors, critical regions, p-values, assumptions of t-tests, and procedures for one-sample t-tests, independent and paired t-tests, one-way ANOVA, and repeated measures ANOVA. The document is intended as part of an online workshop on using SPSS for advanced statistical data analysis.
This document provides an overview of how to interpret the results of a hypothesis test for a population proportion. It discusses key concepts like the null and alternative hypotheses, types of tests (one-tailed and two-tailed), significance levels, critical values, and rejection regions. Examples are provided to illustrate how to set up the hypotheses, determine critical values, identify the rejection region, and make a decision to reject or fail to reject the null hypothesis based on the test statistic value. The goal is to help learners understand how to correctly interpret the outcome of a test on a population proportion.
The document discusses hypothesis testing and outlines the key steps in the hypothesis testing process:
1) Formulating the null and alternative hypotheses about a population parameter. The null hypothesis is tested while the alternative is accepted if the null is rejected.
2) Determining the significance level and critical value based on this level which establishes the boundary for rejecting the null hypothesis.
3) Selecting a sample, calculating the test statistic and comparing it to the critical value to determine whether to reject or fail to reject the null hypothesis.
4) Hypothesis tests can be one-tailed, focusing rejection in one tail, or two-tailed, splitting rejection between both tails. Steps are generally the same but null and alternatives differ.
The document discusses the sign test, a nonparametric hypothesis test that does not require assumptions about the population distribution. The sign test can be used to test claims involving matched pairs, nominal data with two categories, or the population median. The document provides guidelines for performing the sign test in each of these cases, including stating hypotheses, determining sample sizes and test statistics, and making conclusions. Examples are also given to illustrate the sign test for matched pairs, nominal data, and testing the population median.
This document discusses hypothesis testing, including:
- The definition of a hypothesis as a testable assumption or prediction between two variables. Null and alternative hypotheses are introduced.
- Directional and non-directional hypothesis tests are explained, with examples given.
- The two types of errors in hypothesis testing - Type I and Type II errors - are defined and examples provided.
- The three main approaches to hypothesis testing are outlined: test statistic, p-value, and confidence interval approaches. Key aspects of each approach are highlighted at a high level.
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 8: Hypothesis Testing
8.1: Basics of Hypothesis Testing
Sampling of Attributes Hypothesis Test of Hypothesis One tailed ...HarshaKrishnaBhatP1
HYPOTHESIS
A hypothesis is an idea or explanation that you then test through study and experimentation. Outside of science, theory or guess can also be called a hypothesis. A hypothesis is something more than a wild guess but less than a well-established theory.
.
.
.
Types
1)one-tailed
2)Two-tailed
.
.
One-tailed test alludes to the significance test in which the region of rejection appears on one end of the sampling distribution. It represents that the estimated test parameter is greater or less than the critical value. When the sample tested falls in the region of rejection, i.e. either left or right side, as the case may be, it leads to the acceptance of the alternative hypothesis rather than the null hypothesis.
..
.
.
examples...
This document provides an overview of statistical inference and hypothesis testing. It discusses key concepts such as the null and alternative hypotheses, type I and type II errors, one-tailed and two-tailed tests, test statistics, p-values, confidence intervals, and parametric vs non-parametric tests. Specific statistical tests covered include the t-test, z-test, ANOVA, chi-square test, and correlation analyses. The document also addresses how sample size affects test power and significance.
Day-2_Presentation for SPSS parametric workshop.pptxrjaisankar
This document provides an overview of parametric statistical tests, including t-tests and analysis of variance (ANOVA). It discusses the concepts of statistical inference, hypothesis testing, null and alternative hypotheses, types of errors, critical regions, p-values, assumptions of t-tests, and procedures for one-sample t-tests, independent and paired t-tests, one-way ANOVA, and repeated measures ANOVA. The document is intended as part of an online workshop on using SPSS for advanced statistical data analysis.
This document provides an overview of how to interpret the results of a hypothesis test for a population proportion. It discusses key concepts like the null and alternative hypotheses, types of tests (one-tailed and two-tailed), significance levels, critical values, and rejection regions. Examples are provided to illustrate how to set up the hypotheses, determine critical values, identify the rejection region, and make a decision to reject or fail to reject the null hypothesis based on the test statistic value. The goal is to help learners understand how to correctly interpret the outcome of a test on a population proportion.
The document discusses hypothesis testing and outlines the key steps in the hypothesis testing process:
1) Formulating the null and alternative hypotheses about a population parameter. The null hypothesis is tested while the alternative is accepted if the null is rejected.
2) Determining the significance level and critical value based on this level which establishes the boundary for rejecting the null hypothesis.
3) Selecting a sample, calculating the test statistic and comparing it to the critical value to determine whether to reject or fail to reject the null hypothesis.
4) Hypothesis tests can be one-tailed, focusing rejection in one tail, or two-tailed, splitting rejection between both tails. Steps are generally the same but null and alternatives differ.
The document discusses the sign test, a nonparametric hypothesis test that does not require assumptions about the population distribution. The sign test can be used to test claims involving matched pairs, nominal data with two categories, or the population median. The document provides guidelines for performing the sign test in each of these cases, including stating hypotheses, determining sample sizes and test statistics, and making conclusions. Examples are also given to illustrate the sign test for matched pairs, nominal data, and testing the population median.
This document discusses hypothesis testing, including:
- The definition of a hypothesis as a testable assumption or prediction between two variables. Null and alternative hypotheses are introduced.
- Directional and non-directional hypothesis tests are explained, with examples given.
- The two types of errors in hypothesis testing - Type I and Type II errors - are defined and examples provided.
- The three main approaches to hypothesis testing are outlined: test statistic, p-value, and confidence interval approaches. Key aspects of each approach are highlighted at a high level.
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 8: Hypothesis Testing
8.1: Basics of Hypothesis Testing
Sampling of Attributes Hypothesis Test of Hypothesis One tailed ...HarshaKrishnaBhatP1
HYPOTHESIS
A hypothesis is an idea or explanation that you then test through study and experimentation. Outside of science, theory or guess can also be called a hypothesis. A hypothesis is something more than a wild guess but less than a well-established theory.
.
.
.
Types
1)one-tailed
2)Two-tailed
.
.
One-tailed test alludes to the significance test in which the region of rejection appears on one end of the sampling distribution. It represents that the estimated test parameter is greater or less than the critical value. When the sample tested falls in the region of rejection, i.e. either left or right side, as the case may be, it leads to the acceptance of the alternative hypothesis rather than the null hypothesis.
..
.
.
examples...
This document provides an overview of statistical inference and hypothesis testing. It discusses key concepts such as the null and alternative hypotheses, type I and type II errors, one-tailed and two-tailed tests, test statistics, p-values, confidence intervals, and parametric vs non-parametric tests. Specific statistical tests covered include the t-test, z-test, ANOVA, chi-square test, and correlation analyses. The document also addresses how sample size affects test power and significance.
This document provides information about statistical hypothesis testing procedures including t-tests, z-tests, and F-tests. It defines key terms like the null hypothesis, alternative hypothesis, and level of significance. It explains the steps to conduct hypothesis tests including setting the hypotheses, fixing the significance level, choosing a test criterion, performing calculations, and making a conclusion. Specifically, it discusses one-sample and two-sample z-tests for large samples and one-sample and two-sample t-tests for small samples. The assumptions, conditions, and procedures for each test are outlined in detail over multiple pages.
For more classes visit
www.snaptutorial.com
1
To make tests of hypotheses about more than two population means, we use the:
t distribution
normal distribution
chi-square distribution
analysis of variance distribution
2
You randomly select two households and observe whether or not they own a telephone answering machine. Which of the following is a simple event?
At most one of them owns a telephone answering machine.
The document discusses hypothesis testing, including defining the null and alternative hypotheses, types of errors, test statistics, and the process of hypothesis testing. Some key points:
- The null hypothesis states that a population parameter is equal to a specific value. The alternative hypothesis is paired with the null and states inequality.
- Type I errors occur when the null hypothesis is rejected when it is true. Type II errors occur when the null is not rejected when it is false.
- A test statistic is calculated based on sample data and compared to critical values to determine if the null hypothesis can be rejected.
- Hypothesis testing follows steps of stating hypotheses, choosing a significance level, collecting/analyzing data,
This document discusses the process of testing hypotheses. It begins by defining hypothesis testing as a way to make decisions about population characteristics based on sample data, which involves some risk of error. The key steps are outlined as:
1) Formulating the null and alternative hypotheses, with the null hypothesis stating no difference or relationship.
2) Computing a test statistic based on the sample data and selecting a significance level, usually 5%.
3) Comparing the test statistic to critical values to either reject or fail to reject the null hypothesis.
Examples are provided to demonstrate hypothesis testing for a single mean, comparing two means, and testing a claim about population characteristics using sample data and statistics.
For more classes visit
www.snaptutorial.com
1
To make tests of hypotheses about more than two population means, we use the:
t distribution
normal distribution
chi-square distribution
analysis of variance distribution
Basic of Statistical Inference Part-IV: An Overview of Hypothesis TestingDexlab Analytics
The fourth part of the basic of statistical inference series puts its focus on discussing the concept of hypothesis testing explaining all the nuances.
Following points are presented in this presentation.
1. Hypothesis testing is a decision-making process for evaluating claims about a population.
2. NULL HYPOTHESIS & ALTERNATIVE HYPOTHESIS.
3. Types of errors.
This document discusses hypothesis testing and the t-test. It covers:
1) The basics of hypothesis testing including null and alternative hypotheses, types of hypotheses, and types of errors.
2) The t-test, which is used for small samples from a normally distributed population. It relies on the t-distribution and the degree of freedom.
3) Applications of the t-test including testing the significance of a single mean, difference between two means, and paired t-tests.
4) When sample sizes are large, the normal distribution can be used instead in Z-tests for similar applications.
This document provides an overview of hypothesis testing. It defines a hypothesis as a prediction based on knowledge or observations that is then tested. The document outlines the key steps in hypothesis testing as stating the null and alternative hypotheses, choosing an appropriate test statistic, specifying the significance level and sample size, selecting the sampling distribution and critical region, and deciding whether to reject or accept the null hypothesis. It also defines the different types of hypotheses and statistical tests commonly used in experimental research.
Hypothesis testing involves making an assumption about an unknown population parameter, called the null hypothesis (H0). A hypothesis is tested by collecting a sample from the population and comparing sample statistics to the null hypothesis. If the sample statistic is sufficiently different from the null hypothesis, the null hypothesis is rejected. There are two types of errors that can occur - type 1 errors occur when a true null hypothesis is rejected, and type 2 errors occur when a false null hypothesis is not rejected. Hypothesis tests can be one-tailed, testing if the sample statistic is greater than or less than the null hypothesis, or two-tailed, testing if it is significantly different in either direction.
This document provides a 100-question practice exam for the QNT 275 final exam. It covers topics in statistics including hypothesis testing, data types, measurement scales, sampling, descriptive statistics, and inferential statistics. Sample questions are multiple choice and cover topics like hypothesis tests, measurement scales, sampling methods, descriptive vs inferential statistics, and data analysis techniques like ANOVA. The practice exam allows students to test their understanding of key statistical concepts.
Hypothesis testing involves making an assumption about an unknown population parameter, called the null hypothesis (H0). A hypothesis is tested by collecting a sample from the population and comparing sample statistics to the hypothesized parameter value. If the sample value differs significantly from the hypothesized value based on a predetermined significance level, then the null hypothesis is rejected. There are two types of errors that can occur - type 1 errors occur when a true null hypothesis is rejected, and type 2 errors occur when a false null hypothesis is not rejected. Hypothesis tests can be one-tailed, testing if the sample value is greater than or less than the hypothesized value, or two-tailed, testing if the sample value is significantly different from the hypothesized value.
Testing of Hypothesis, p-value, Gaussian distribution, null hypothesissvmmcradonco1
This document provides an overview of key concepts in statistical hypothesis testing. It defines what a hypothesis is, the different types of hypotheses (null, alternative, one-tailed, two-tailed), and statistical terms used in hypothesis testing like test statistics, critical regions, significance levels, critical values, type I and type II errors. It also explains the decision making process in hypothesis testing, such as rejecting or failing to reject the null hypothesis based on whether the test statistic falls within the critical region or if the p-value is less than the significance level.
Tests of statistical significance : chi square and spss Drsnehas2
This document discusses tests of statistical significance, specifically chi square tests. It defines key concepts like p-values, type I and type II errors, and null and alternative hypotheses. It explains how to calculate chi square manually and in SPSS. Chi square can be used to test for differences in proportions between groups and associations between variables. The document provides examples of chi square tests and interpreting their results.
1) Hypothesis testing involves specifying a null hypothesis (H0) and an alternative hypothesis (H1). The null hypothesis states that there is no difference or relationship, while the alternative hypothesis specifies a difference or relationship.
2) A statistical test is used to determine whether to reject the null hypothesis based on sample data. There is a risk of making Type I or Type II errors.
3) The p-value represents the probability of obtaining a test statistic at least as extreme as the one that was actually observed, assuming that the null hypothesis is true.
Hypothesis testing involves making an assumption about an unknown population parameter, called the null hypothesis (H0). A hypothesis test is then conducted by collecting a sample from the population and calculating a test statistic. The test statistic is compared to a critical value to either reject or fail to reject the null hypothesis. There are two types of errors that can occur - a Type I error occurs when a true null hypothesis is rejected, and a Type II error occurs when a false null hypothesis is not rejected. The level of significance and whether the test is one-tailed or two-tailed determine the critical value used for comparison.
Basics of Hypothesis testing for PharmacyParag Shah
This presentation will clarify all basic concepts and terms of hypothesis testing. It will also help you to decide correct Parametric & Non-Parametric test for your data
The document discusses sampling and hypothesis testing. It defines key concepts like population, sample, parameter, statistic, sampling distribution, null hypothesis, alternative hypothesis, type I and type II errors. It explains different sampling methods and how to test hypotheses about population means using z-tests. Examples are provided to illustrate hypothesis testing for single and two population means. The summary tests hypotheses about population means using z-scores and critical values at given significance levels.
Understanding User Needs and Satisfying ThemAggregage
https://www.productmanagementtoday.com/frs/26903918/understanding-user-needs-and-satisfying-them
We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.
In this webinar, we won't focus on the research methods for discovering user-needs. We will focus on synthesis of the needs we discover, communication and alignment tools, and how we operationalize addressing those needs.
Industry expert Scott Sehlhorst will:
• Introduce a taxonomy for user goals with real world examples
• Present the Onion Diagram, a tool for contextualizing task-level goals
• Illustrate how customer journey maps capture activity-level and task-level goals
• Demonstrate the best approach to selection and prioritization of user-goals to address
• Highlight the crucial benchmarks, observable changes, in ensuring fulfillment of customer needs
Best practices for project execution and deliveryCLIVE MINCHIN
A select set of project management best practices to keep your project on-track, on-cost and aligned to scope. Many firms have don't have the necessary skills, diligence, methods and oversight of their projects; this leads to slippage, higher costs and longer timeframes. Often firms have a history of projects that simply failed to move the needle. These best practices will help your firm avoid these pitfalls but they require fortitude to apply.
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This document provides information about statistical hypothesis testing procedures including t-tests, z-tests, and F-tests. It defines key terms like the null hypothesis, alternative hypothesis, and level of significance. It explains the steps to conduct hypothesis tests including setting the hypotheses, fixing the significance level, choosing a test criterion, performing calculations, and making a conclusion. Specifically, it discusses one-sample and two-sample z-tests for large samples and one-sample and two-sample t-tests for small samples. The assumptions, conditions, and procedures for each test are outlined in detail over multiple pages.
For more classes visit
www.snaptutorial.com
1
To make tests of hypotheses about more than two population means, we use the:
t distribution
normal distribution
chi-square distribution
analysis of variance distribution
2
You randomly select two households and observe whether or not they own a telephone answering machine. Which of the following is a simple event?
At most one of them owns a telephone answering machine.
The document discusses hypothesis testing, including defining the null and alternative hypotheses, types of errors, test statistics, and the process of hypothesis testing. Some key points:
- The null hypothesis states that a population parameter is equal to a specific value. The alternative hypothesis is paired with the null and states inequality.
- Type I errors occur when the null hypothesis is rejected when it is true. Type II errors occur when the null is not rejected when it is false.
- A test statistic is calculated based on sample data and compared to critical values to determine if the null hypothesis can be rejected.
- Hypothesis testing follows steps of stating hypotheses, choosing a significance level, collecting/analyzing data,
This document discusses the process of testing hypotheses. It begins by defining hypothesis testing as a way to make decisions about population characteristics based on sample data, which involves some risk of error. The key steps are outlined as:
1) Formulating the null and alternative hypotheses, with the null hypothesis stating no difference or relationship.
2) Computing a test statistic based on the sample data and selecting a significance level, usually 5%.
3) Comparing the test statistic to critical values to either reject or fail to reject the null hypothesis.
Examples are provided to demonstrate hypothesis testing for a single mean, comparing two means, and testing a claim about population characteristics using sample data and statistics.
For more classes visit
www.snaptutorial.com
1
To make tests of hypotheses about more than two population means, we use the:
t distribution
normal distribution
chi-square distribution
analysis of variance distribution
Basic of Statistical Inference Part-IV: An Overview of Hypothesis TestingDexlab Analytics
The fourth part of the basic of statistical inference series puts its focus on discussing the concept of hypothesis testing explaining all the nuances.
Following points are presented in this presentation.
1. Hypothesis testing is a decision-making process for evaluating claims about a population.
2. NULL HYPOTHESIS & ALTERNATIVE HYPOTHESIS.
3. Types of errors.
This document discusses hypothesis testing and the t-test. It covers:
1) The basics of hypothesis testing including null and alternative hypotheses, types of hypotheses, and types of errors.
2) The t-test, which is used for small samples from a normally distributed population. It relies on the t-distribution and the degree of freedom.
3) Applications of the t-test including testing the significance of a single mean, difference between two means, and paired t-tests.
4) When sample sizes are large, the normal distribution can be used instead in Z-tests for similar applications.
This document provides an overview of hypothesis testing. It defines a hypothesis as a prediction based on knowledge or observations that is then tested. The document outlines the key steps in hypothesis testing as stating the null and alternative hypotheses, choosing an appropriate test statistic, specifying the significance level and sample size, selecting the sampling distribution and critical region, and deciding whether to reject or accept the null hypothesis. It also defines the different types of hypotheses and statistical tests commonly used in experimental research.
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This document provides a 100-question practice exam for the QNT 275 final exam. It covers topics in statistics including hypothesis testing, data types, measurement scales, sampling, descriptive statistics, and inferential statistics. Sample questions are multiple choice and cover topics like hypothesis tests, measurement scales, sampling methods, descriptive vs inferential statistics, and data analysis techniques like ANOVA. The practice exam allows students to test their understanding of key statistical concepts.
Hypothesis testing involves making an assumption about an unknown population parameter, called the null hypothesis (H0). A hypothesis is tested by collecting a sample from the population and comparing sample statistics to the hypothesized parameter value. If the sample value differs significantly from the hypothesized value based on a predetermined significance level, then the null hypothesis is rejected. There are two types of errors that can occur - type 1 errors occur when a true null hypothesis is rejected, and type 2 errors occur when a false null hypothesis is not rejected. Hypothesis tests can be one-tailed, testing if the sample value is greater than or less than the hypothesized value, or two-tailed, testing if the sample value is significantly different from the hypothesized value.
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This document discusses tests of statistical significance, specifically chi square tests. It defines key concepts like p-values, type I and type II errors, and null and alternative hypotheses. It explains how to calculate chi square manually and in SPSS. Chi square can be used to test for differences in proportions between groups and associations between variables. The document provides examples of chi square tests and interpreting their results.
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Hypothesis testing involves making an assumption about an unknown population parameter, called the null hypothesis (H0). A hypothesis test is then conducted by collecting a sample from the population and calculating a test statistic. The test statistic is compared to a critical value to either reject or fail to reject the null hypothesis. There are two types of errors that can occur - a Type I error occurs when a true null hypothesis is rejected, and a Type II error occurs when a false null hypothesis is not rejected. The level of significance and whether the test is one-tailed or two-tailed determine the critical value used for comparison.
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2. Every hypothesis-testing
situation begins with the
statement of a hypothesis.
A Statistical Hypothesis is a
conjecture about a population
parameter.
This conjecture may or may not
be true.
2
STATISTICAL HYPOTHESIS
3. There are two types of statistical
hypotheses for each situation:
1. Null hypothesis
2. Alternative hypothesis
3
TYPES OF STATISTICAL HYPOTHESIS
4. The Null Hypothesis,
symbolized by H0, is a statistical
hypothesis that states that there
is no difference between a
parameter and a specific value,
or that there is no difference
between two parameters.
It is also called ideal situation.
4
TYPES OF STATISTICAL HYPOTHESIS
NULL HYPOTHESIS
5. The Alternative Hypothesis,
symbolized by H1, is a statistical
hypothesis that states the
existence of a difference between
a parameter and a specific value,
or states that there is a difference
between two parameters.
It is also called claim of
researcher or research
Hypothesis.
5
TYPES OF STATISTICAL HYPOTHESIS
ALTERNATIVE HYPOTHESIS
6. A researcher of software engineering
is interested in finding out whether a
new IDE ( Eclipse) will change the
performance of programmers. The
researcher is particularly concerned
with the ‘LOC per hour’ a
programmer writes who use the new
IDE. Will the LOC per hour increase,
decrease, or remain unchanged after
a programmer uses new IDE.
6
ILLUSTRATION OF HYPOTHESES
CASE STUDY # 1
7. Since the researcher knows that the
mean LOC per hour for the
population under study is 26 LOC
per hour, the hypotheses for this
situation are
H0: 𝜇 = 26
H1: 𝜇 ≠ 26
7
ILLUSTRATION OF HYPOTHESES
CASE STUDY # 1
8. The null hypothesis specifies that
the mean will remain unchanged,
and the alternative hypothesis states
that it will be different.
This test is called a two-tailed test,
since the possible result of new IDE
could be to raise or lower the
performance of programmers.
8
ILLUSTRATION OF HYPOTHESES
CASE STUDY # 1
9. R&D department of a software
company proposed that the usage of
an automation tool (i.e. QTP) in
testing cycle of a software project
increases the identification of bugs
in each module.
If the mean bugs of each module
without the usage of QTP is 146
bugs, then his hypotheses are;
H0: 𝜇 = 146 H1: 𝜇 > 146
9
ILLUSTRATION OF HYPOTHESES
CASE STUDY # 2
10. In this situation, R&D department is
interested only in increasing the
bugs identification of modules, so his
alternative hypothesis is that mean
is greater than 146 bugs. The null
hypothesis is that the mean is equal
to 146.
This test is called right-tailed, since
the interest is in an increase only.
10
ILLUSTRATION OF HYPOTHESES
CASE STUDY # 2
11. Software Development department
of a software company wishes to
lower open bugs by using a new unit
testing technique. If the average
bugs per UseCase are 13, the
hypotheses about bugs count with
the use of new Unit Testing
technique are:
H0: 𝜇 = 13 H1: 𝜇 < 13
11
ILLUSTRATION OF HYPOTHESES
CASE STUDY # 3
12. This test is left-tailed test, since
software development department is
interested only in lowering the bugs
count in each UseCase.
12
ILLUSTRATION OF HYPOTHESES
CASE STUDY # 3
13. The null and alternative
hypotheses are stated together,
and the null hypothesis contains
the equals sign.
13
SIGNS IN ILLUSTRATION OF HYPOTHESIS
15. A Statistical Test uses the data
obtained from a sample to make
a decision about whether the null
hypothesis should be rejected.
The numerical value obtained
from a statistical test is called
the Test Value.
15
STATISTICAL TEST
16. In statistical test, the mean is
computed for the data obtained from
the sample and is compared with the
population mean.
Then a decision is made to reject or
not reject the null hypothesis on the
basis of the value obtained from the
statistical test.
If the difference is significant, the
null hypothesis is rejected. If it is
not, then the null hypothesis is not
rejected.
16
DECISION IN HYPOTHESIS TESTING
17. In the hypothesis-testing situation,
there are four possible outcomes.
In reality, the null hypothesis may
or may not be true, and a decision
is made to reject or not reject it on
the basis of the data obtained from
a sample.
There are two possibilities for a
correct decision and two
possibilities for an incorrect
decision ( shown in decision matrix)
17
DECISION IN HYPOTHESIS TESTING
19. A type I error occurs if you
reject the null hypothesis when it
is true.
A type II error occurs if you do
not reject the null hypothesis
when it is false.
19
TYPE I & TYPE II ERRORS
20. The level of significance is the
maximum probability of
committing a type I error.
This probability is symbolized by
α (alpha).
P(type I error) = α.
20
LEVEL OF SIGNIFICANCE
21. Statisticians generally agree on
using three arbitrary significance
levels: the 0.10, 0.05, and 0.01
levels.
That is, if the null hypothesis is
rejected, the probability of a type I
error will be 10%, 5%, or 1%,
depending on which level of
significance is used.
21
LEVEL OF SIGNIFICANCE
22. In other words:
When α = 0.10, there is a 10% chance
of rejecting a true null hypothesis
When α = 0.05, there is a 5% chance of
rejecting a true null hypothesis
When α = 0.01, there is a 1% chance of
rejecting a true null hypothesis.
22
LEVEL OF SIGNIFICANCE
23. In hypothesis-testing, the
researcher decides what level of
significance to use.
It can be any level, depending on
the seriousness of the type I error.
After a significance level is chosen,
a critical value is selected from a
table for the appropriate test.
23
LEVEL OF SIGNIFICANCE
24. The critical value separates the
critical region from the noncritical
region.
The symbol for critical value is C.V.
24
CRITICAL VALUE
25. The critical or rejection region is
the range of values of the test value
that indicates that there is a
significant difference and that the
null hypothesis should be rejected.
The noncritical or nonrejection
region is the range of values of the
test value that indicates that the
difference was probably due to
chance and that the null hypothesis
should not be rejected.
25
CRITICAL & NONCRITICAL REGIONS
26. A one-tailed test indicates that the
null hypothesis should be rejected
when the test value is in the critical
region on one side of the mean.
A one-tailed test is either a right-
tailed test or left-tailed test,
depending on the direction of the
inequality of the alternative
hypothesis.
26
ONE-TAILED TEST
27. To obtain the critical value, the
researcher must choose an alpha
level.
Suppose the researcher chose α =
0.01, for a right tailed test.
Then the researcher must find a z
value such that 1% of the area falls
to the right of the z value and 99%
falls to the left of the z value
27
FINDING CRITICAL VALUE FOR α = 0.01
RIGHT-TAILED TEST
28. The researcher must find the area
value in Table closest to 0.9900.
The critical z value is 2.33,
28
FINDING CRITICAL VALUE FOR α = 0.01
RIGHT-TAILED TEST
32. In left-tailed test, the critical value
falls to the left of mean.
At α = 0.01, the critical value is -
2.33
32
FINDING CRITICAL VALUE FOR α = 0.01
LEFT-TAILED TEST
34. In a two-tailed test, the null
hypothesis should be rejected when
the test value is in either of the two
critical regions.
For a two-tailed test, the critical
region must be split into two equal
parts.
If α = 0.01, then one-half of the area, or
0.005, must be to the right of the mean
and one half must be to the left of the
mean.
34
TWO-TAILED TEST
35. If α = 0.01, then one-half of the area, or
0.005, must be to the right of the mean
and one half must be to the left of the
mean.
In this case, the z value on the left
side is found by looking up the z
value corresponding to an area of
0.0050. This value is -2.58.
On the right side, it is necessary to
find the z value corresponding to
0.99 + 0.005, or 0.9950. It is +2.58
35
TWO-TAILED TEST
41. Step 1: Draw the figure and indicate
the appropriate area.
If the test is left-tailed, the critical region,
with an area equal to α, will be on the left
side of the mean.
If the test is right-tailed, the critical region,
with an area equal to α, will be on the right
side of the mean.
If the test is two-tailed, α must be divided
by 2; one-half of the area will be to the
right of the mean, and one-half will be to
the left of the mean.
41
PROCEDURE for Finding CRITICAL VALUE for SPECIFIC α
42. Step 2: Find the value of Z.
For a left-tailed test, use the z value that
corresponds to the area equivalent to α in
Table.
For a right-tailed test, use the z value that
corresponds to the area equivalent to 1 - α.
For a two-tailed test, use the z value that
corresponds to α/2 for the left value. For
the right value, use z value that
corresponds to the area equivalent to 1-
α/2.
42
PROCEDURE for Finding CRITICAL VALUE for SPECIFIC α
43. Using Table, find the critical value(s)
for given situation and draw the
appropriate figure, showing the
critical region.
A left-tailed test with α = 0.10.
43
FINDING CRITICAL VALUE
EXERCISE # 1
44. Draw the figure and indicate the
appropriate area. Since this is a left-
tailed test, the area of 0.10 is
located in the left tail.
Find the area closest to 0.1000 in
Table. In this case, it is 0.1003. Find
the z value that corresponds to the
area 0.1003. It is -1.28.
44
FINDING CRITICAL VALUE
SOLUTION OF EXERCISE # 1
46. Using Table, find the critical value(s)
for given situation and draw the
appropriate figure, showing the
critical region.
A two-tailed test with α = 0.02.
46
FINDING CRITICAL VALUE
EXERCISE # 2
47. Draw the figure and indicate the
appropriate area.
In this case, there are two areas
equivalent to α/2, or 0.02/2 = 0.01.
For the left z critical value, find the
area closest to α/2, or 0.02/2 =
0.01.
In this case, it is 0.0099.
47
FINDING CRITICAL VALUE
SOLUTION OF EXERCISE # 2
48. For the right z critical value, find the
area closest to 1-α/2, or 1-0.02/2 =
0.9900.
In this case, it is 0.9901.
Find the z values for each of the
areas.
For 0.0099, z =-2.33.
For the area of 0.9901, z =2.33.
48
FINDING CRITICAL VALUE
SOLUTION OF EXERCISE # 2
50. Using Table, find the critical value(s)
for given situation and draw the
appropriate figure, showing the
critical region.
A right-tailed test with α = 0.005.
50
FINDING CRITICAL VALUE
EXERCISE # 3
51. Draw the figure and indicate the
appropriate area. Since this is a
right-tailed test, the area 0.005 is
located in the right tail.
Find the area closest to 1-α, or 1-
0.005 = 0.9950. In this case, it is
0.9951.
Find the z value that corresponds to
the area 0.9951. It is 2.58.
51
FINDING CRITICAL VALUE
SOLUTION OF EXERCISE # 3
54. Step 1 State the hypotheses and
identify the claim.
Step 2 Find the critical value(s)
from the appropriate table.
Step 3 Compute the test value.
Step 4 Make the decision to reject
or not reject the null hypothesis.
Step 5 Summarize the results.
54
STEPS IN HYPOTHESIS TESTING
55. Test value=((Observed value)–(Expected Value))/Std Error
Observed Value is the statistic that is
computed from the sample data
The expected value is the parameter
(such as the population mean) that
you would expect to obtain if the null
hypothesis were true.
The denominator is the standard error
of the statistic being tested (i.e.
standard error of the mean)
55
FORMULA for ‘TEST VALUE’
56. The z Test is a statistical test
for the mean of a population.
It can be used when n >= 30,
or when the population is
normally distributed and 𝜎 is
known.
56
Z TEST FOR A MEAN
58. For the z test, the observed
value is the value of the sample
mean.
The expected value is the value
of the population mean,
assuming that the null
hypothesis is true.
The denominator 𝜎/√n is the
standard error of the mean.
58
Z TEST FOR A MEAN
59. A researcher wishes to see if the
mean number of days that a test
cycle of ‘Message Portal’ is
completed in 29 days.
A sample of 30 test cycles has a
mean of 30.1 days.
At α = 0.05, test the claim that the
mean time is greater than 29 days.
The standard deviation of the
population is 3.8 days.
59
HYPOTHESIS TESTING
EXERCISE # 4
60. Step 1 State the hypotheses and
identify the claim
H0: 𝜇 = 29 H1: 𝜇 > 29 (Claim)
Step 2 Find the critical value.
Since α = 0.05 and the test is a right-
tailed test
The critical value is z =1.65.
60
HYPOTHESIS TESTING
SOLUTION OF EXERCISE # 4
61. Step 3 Compute the Test value
Step 4 Make the decision
Since the test value, 1.59, is less than
the critical value, 1.65, and is not in the
critical region
Therefore, the decision is to not reject
the null hypothesis.
61
HYPOTHESIS TESTING
SOLUTION OF EXERCISE # 4
62. Step 5 Summarize the results.
There is not enough evidence to
support the claim that the mean time is
greater than 29 days.
62
HYPOTHESIS TESTING
SOLUTION OF EXERCISE # 4
63. A researcher claims that the
average cost of a new change in
requirements of ‘Message Portal’
software is less than $80.
He selects a random sample of 36
changes from repository and finds
the following costs (in dollars).
Is there enough evidence to support
the researcher’s claim at α = 0.10?
Assume 𝜎 = 19.2.
63
HYPOTHESIS TESTING
EXERCISE # 5
65. Step 1 State the hypotheses and
identify the claim
H0: 𝜇 = 80 H1: 𝜇 < 80 (Claim)
Step 2 Find the critical value.
Since α = 0.10 and the test is a left-
tailed test
The critical value is z =-1.28
65
HYPOTHESIS TESTING
SOLUTION OF EXERCISE # 5
66. Step 3 Compute the Test value
Compute the Mean of Sample Data
Mean = 75.0, 𝜎 = 19.2
Substitute the values in formula
66
HYPOTHESIS TESTING
SOLUTION OF EXERCISE # 5
67. Step 4 Make the decision
Since the test value, -1.56, falls in the
critical region
The decision is to reject the null
hypothesis.
Step 5 Summarize the results.
There is enough evidence to support
the claim that the average cost of new
change is less than $80.
67
HYPOTHESIS TESTING
SOLUTION OF EXERCISE # 5
69. Business Development department
of software house reports that the
average cost of establishment of a
small Data Center is $24,672.
To see if the average cost of
establishment of a small Data
Center is different at a particular
site, a researcher selects a random
sample of 35 sites and finds that the
average cost is $26,343.
69
HYPOTHESIS TESTING
EXERCISE # 6
70. The standard deviation of the
population is $3251.
At α = 0.01, can it be concluded
that the average cost of
establishment of a new small Data
Center is different from $24,672?
70
HYPOTHESIS TESTING
EXERCISE # 6
71. Step 1 State the hypotheses and
identify the claim
H0: 𝜇 = $24,672
H1: 𝜇 != $24,672 (Claim)
Step 2 Find the critical value.
Since α = 0.10 and the test is a two-
tailed test
The critical values are +2.58 and -2.58
71
HYPOTHESIS TESTING
SOLUTION OF EXERCISE # 6
72. Step 3 Compute the Test value
Step 4 Make the decision.
Reject the null hypothesis, since the
test value falls in the critical region
72
HYPOTHESIS TESTING
SOLUTION OF EXERCISE # 6
74. Step 5 Summarize the results.
There is enough evidence to support
the claim that the average cost for
establishment of new Data Center is
different from $24,672.
74
HYPOTHESIS TESTING
SOLUTION OF EXERCISE # 6
76. The P-value (or probability
value) is the probability of
getting a sample statistic (such
as the mean) in the direction of
the alternative hypothesis when
the null hypothesis is true.
76
P-VALUE METHOD FOR HYPOTHESIS TESTING
77. Step 1 State the hypotheses and
identify the claim.
Step 2 Compute the test value.
Step 3 Find the P-Value.
Step 4 Make the decision to reject
or not reject the null hypothesis.
Step 5 Summarize the results.
77
STEPS IN P-VALUE METHOD
78. A researcher wishes to test the
claim that the average cost of an
ERP software is greater than $5700.
She selects a random sample of 36
ERP solutions and finds the mean to
be $5950. The population standard
deviation is $659.
Is there evidence to support the
claim at α = 0.05?
Use the P-value method.
78
HYPOTHESIS TESTING
EXERCISE # 7
79. Step 1 State the hypotheses and
identify the claim
H0: 𝜇 = $5700
H1: 𝜇 > $5700 (Claim)
Step 2 Compute the test value.
79
HYPOTHESIS TESTING
SOLUTION OF EXERCISE # 7
80. Step 3 Find the P-value
Find the corresponding area under the
normal distribution for z = 2.28. It is
0.9887
Subtract this value for the area from
1.0000 to find the area in the right tail.
1.0000 - 0.9887 = 0.0113
Hence the P-value is 0.0113.
80
HYPOTHESIS TESTING
SOLUTION OF EXERCISE # 7
81. Step 4 Make the decision
Since the P-value is less than 0.05, the
decision is to reject the null hypothesis.
81
HYPOTHESIS TESTING
SOLUTION OF EXERCISE # 7
82. Step 5 Summarize the results
There is enough evidence to support
the claim that the average cost of ERP
solutions are greater than $5700.
82
HYPOTHESIS TESTING
SOLUTION OF EXERCISE # 7
83. If P-value <= α, reject the null
hypothesis.
If P-value >= α, do not reject the
null hypothesis.
83
DECISION RULE
WHEN USING P-VALUE
85. The t test is a statistical test for the
mean of a population and is used
when the population is normally or
approximately normally distributed,
and 𝜎 is unknown.
The formula for the t test is
The degree of freedom is d.f.=n - 1.
85
T TEST FOR A MEAN
86. Find the critical t value for α = 0.05
with d.f. = 16 for a right-tailed t
test.
86
FINDING CRITICAL VALUE FOR THE T TEST
EXERCISE # 8
87. Find the 0.05 column in the top row
and 16 in the left-hand column.
Where the row and column meet,
the appropriate critical value is
found; it is 1.746.
87
FINDING CRITICAL VALUE FOR THE T TEST
SOLUTION OF EXERCISE # 8
89. Find the critical t value for α = 0.01
with d.f. = 22 for a left-tailed test.
Critical value is -2.508
89
FINDING CRITICAL VALUE FOR THE T TEST
EXERCISE # 9
90. Find the critical values for α = 0.10
with d.f. = 18 for a two-tailed t test.
Critical values are +1.734 and
1.734
90
FINDING CRITICAL VALUE FOR THE T TEST
EXERCISE # 10
91. Step 1 State the hypotheses and
identify the claim.
Step 2 Find the critical value(s)
from the appropriate table.
Step 3 Compute the test value.
Step 4 Make the decision to reject
or not reject the null hypothesis.
Step 5 Summarize the results.
91
STEPS IN HYPOTHESIS TESTING
92. A researcher claims that the
average cost of software
maintenance is less than $60 per
day. A random sample of cost of
eight days is selected, and shown
below.
60 56 60 55 70 55 60 55
Is there enough evidence to support
the researcher’s claim at α = 0.10?
92
HYPOTHESIS TESTING
EXERCISE # 11
93. Step 1 State the hypotheses and
identify the claim
H0: 𝜇 = $60 H1: 𝜇 < $60 (Claim)
Step 2 Find the critical value.
Since α = 0.10 and d.f. = 7,
Therefore, the critical value is 1.415
93
HYPOTHESIS TESTING
SOLUTION OF EXERCISE # 11
94. Step 3 Compute the Test value
To compute the test value, the mean
and standard deviation must be found.
Mean = $58.88, and s = 5.08
94
HYPOTHESIS TESTING
SOLUTION OF EXERCISE # 11
95. Step 4 Make the decision.
Do not reject the null hypothesis since
0.624 falls in the noncritical region
95
HYPOTHESIS TESTING
SOLUTION OF EXERCISE # 11
96. Step 5 Summarize the results.
There is not enough evidence to
support the researcher’s claim that the
average cost of software maintenance
is less than $60 per day.
96
HYPOTHESIS TESTING
SOLUTION OF EXERCISE # 11