I am glad that I first read the chapters from the text before I viewed the video. Personally, I found the textbook more helpful. But, all in all, the video did a fair job buttressing my understanding of hypothesis testing. The textbook explained the aspects and steps of hypothesis testing in a legible fashion, while the video helped demonstrate a real-life application.

I learned from the text that hypothesis testing is a “Procedure for deciding whether the outcome of a study (results from a sample) supports a particular theory or practical innovation (which is thought to apply to a population)” (Aron A., Aron, E., and Coups, 2011, p. 145). I also learned that hypothesis testing follows a set procedure that appears as follows:

Step 1) Restate the question as a research hypothesis and a null hypothesis about the populations

- Basically, a researcher constructs a hypothesis. Then he/she forms a null hypothesis that opposes the research hypothesis in polar fashion. To help support one’s research hypothesis, one has to disprove the null hypothesis.

Step 2) Determine the characteristics of the comparison distribution

- When using two or more samples, one must gather information about the distribution of means.

Step 3) Determine the cutoff sample score on the comparison distribution at which the null hypothesis should be rejected

- Most researchers choose a significance level of 0.05 or 0.01.

Step 4) Determine your sample’s score on the comparison distribution

Step 5) Decide whether to reject the null hypothesis
(Retrieved from the textbook)

The textbook further introduced me to the notions of one and two-tailed tests, as well as Type I and Type II decision errors, which are highly essential in hypothesis testing.

From the Hypothesis Testing Video, I found out how hypothesis testing is...

...Chapter-11
Testing of Hypothesis:
(Non-parametric Tests)
Chapter-11: Testing of Hypothesis - (Non-parametric Tests)
2
11.1. Chi - square ( χ )Test / Distribution
2
11.1.1. Meaning of Chi - square ( χ )Test
2
11.1.2. Characteristics of Chi - square ( χ )Test
2
11.2. Types of Chi - square ( χ )Test / Distribution
2
11.2.1. Chi - square ( χ )Test for Population Variance
2
11.2.2. Chi - square ( χ )Test for Goodness-of-Fit
2
11.2.3. Chi - square ( χ )Test or Independence
11.3. Analysis of Variance (ANOVA)
11.3.1. Meaning of ANOVA
11.3.2. ANOVA Approach
11.4. ANOVA Technique
11.4.1. One-way ANOVA
11.4.2. Two-way ANOVA
11.4.3. ANOVA in Latin-square Design
11.5. Other Nonparametric Techniques
Summary:
Key Terms:
Questions:
11.1. CHI-SQUARE (
) TEST /DISTRIBUTION
2
11.1.1. Meaning of Chi - square ( χ )Test
2
A chi-square test (also chi squared test or χ test) is any statistical hypothesis test in which the
sampling distribution of the test statistic is a chi-square distribution when the null hypothesis is true,
or any in which this is asymptotically true, meaning that the sampling distribution (if the null
hypothesis is true) can be made to approximate a chi-square distribution as closely as desired by
making the sample size large enough. The Chi-Square (
) test is the most popular non-parametric
test/methods, to test the...

...p-value?
Answer to the question No.4
i. Hypothesistesting:
Step1: State the Null Hypothesis (H0) and Alternative Hypothesis (H1)
H0 : μ ≤ $ 2200 i.e.; mean selling price of homes in Denver is not more than $2200
H1 : μ > $ 2200 i.e.; mean selling price of homes in Denver is more than $2200
Step 2: select the level of significance
Here, the level of significance is, α = .01
Step 3: Determine the appropriate test statistic
t-test statistic will be used here.
Step 4: Formulate the decision rule
If p-value < α – value (or calculated value is greater than critical value), H0 is rejected
If p-value > α- value (or calculated value is less than critical value), H0 is accepted
Step 5: Select the sample, perform the calculations and make a decision
From SPSS output, we get,
One-Sample Statistics
N
Mean
Std. Deviation
Std. Error Mean
Selling Price in TK. 000 (Thousand)
39
2183.323
446.2141
71.4514
One-Sample Test
Test Value = 2200
t
df
Sig. (2-tailed)
Mean Difference
99% Confidence Interval of the Difference
Lower
Upper
Selling Price in TK. 000 (Thousand)
-.233
38
.817
-16.6769
-210.422
177.068
We get, p-value=.817/2=.4085
Decision: P-value (.4085) > α (.01). So we accept H0.
Comment:
So, we can conclude that the mean selling price of homes in Denver is not more than 2200
ii. Hypothesistesting:
Step1: State...

...Business Statistics, 9e (Groebner/Shannon/Fry)
Chapter 10 Estimation and HypothesisTesting for Two Population Parameters
1) The Cranston Hardware Company is interested in estimating the difference in the mean purchase for men customers versus women customers. It wishes to estimate this difference using a 95 percent confidence level. If the sample size is n = 10 from each population, the samples are independent, and sample standard deviations are used, and the variances are assumed equal, then the critical value will be t = 2.1009.
Answer: TRUE
Diff: 2
Keywords: confidence interval, mean difference, independent, sample
Section: 10-1 Estimation for Two Population Means Using Independent Samples
Outcome: 1
2) To find a confidence interval for the difference between the means of independent samples, when the variances are unknown but assumed equal, the sample sizes of the two groups must be the same.
Answer: FALSE
Diff: 2
Keywords: confidence interval, mean difference, independent
Section: 10-1 Estimation for Two Population Means Using Independent Samples
Outcome: 1
3) The Cranston Hardware Company is interested in estimating the difference in the mean purchase for men customers versus women customers. It wishes to estimate this difference using a 95 percent confidence level. Assume that the variances are equal and the populations normally distributed. The following data represent independent samples from each...

...4 Hypothesistesting in the multiple regression model
Ezequiel Uriel
Universidad de Valencia
Version: 09-2013
4.1 Hypothesistesting: an overview
4.1.1 Formulation of the null hypothesis and the alternative hypothesis
4.1.2 Test statistic
4.1.3 Decision rule
4.2 Testing hypotheses using the t test
4.2.1 Test of a single parameter
4.2.2 Confidence intervals
4.2.3 Testinghypothesis about a single linear combination of the parameters
4.2.4 Economic importance versus statistical significance
4.3 Testing multiple linear restrictions using the F test.
4.3.1 Exclusion restrictions
4.3.2 Model significance
4.3.3 Testing other linear restrictions
4.3.4 Relation between F and t statistics
4.4 Testing without normality
4.5 Prediction
4.5.1 Point prediction
4.5.2 Interval prediction
4.5.3 Predicting y in a ln(y) model
4.5.4 Forecast evaluation and dynamic prediction
Exercises
1
2
2
3
5
5
16
17
21
21
22
26
27
28
29
30
30
30
34
34
36
4.1 Hypothesistesting: an overview
Before testing hypotheses in the multiple regression model, we are going to offer
a general overview on hypothesistesting.
Hypothesistesting allows us to carry out inferences about population parameters
using data from a sample. In order to test a...

...HypothesisTesting I
Pat Obi
What is a “Hypothesis?”
A statement or claim about the value of a
population parameter: μ, σ2, p
Pat Obi, Purdue University Calumet
2
Decision Rule
1.
x 0
Z
s
n
Compare calculated Z value to Z value from
Table (critical Z value)
Reject H0 if calculated Z value lies in the
rejection/significance region (i.e. region)
ALTERNATIVELY:
2.
Compare p-value to
Reject H0 if p-value <
Pat Obi, Purdue University Calumet
3
Two-Tail Test
Ex: H0: 0 = 50; H1: 0 ≠ 50. Test at α = 0.05
Reject H0 if calculated Z is either less than ZCV
on the left tail or greater than ZCV on the right
0
Rejection region: /2 = 0.025
Rejection region: /2 = 0.025
0
ZCV = -1.96
ZCV = 1.96
Pat Obi, Purdue University Calumet
4
One-Tail Test: Right/Upper Tail
Ex: H0: 0 ≤ 55; H1: 0 > 55. Test at α = 0.05
Reject H0 if calculated Z > Table Z (i.e. Zcv)
0
Rejection region: = 0.05
ZCV = 1.645
Pat Obi, Purdue University Calumet
5
One-Tail Test: Left/Lower Tail
Ex: H0: 0 ≥ 12; H1: 0 < 12. Test at α = 0.05
Reject H0 if calculated Z < Table Z (i.e. Zcv)
0
Rejection region: = 0.05
ZCV = -1.645
Pat Obi, Purdue University Calumet
6
Z Table (critical Z values)
Significance
Level
Zcv
One-Tail Test
Zcv
Two-Tail Test
0.10
1.285
1.645
0.05
1.645
1.960
0.01
2.326
2.576
Pat Obi, Purdue University Calumet
7
Rules Governing the Statement of
Hypothesis
In...

...A hypothesis is a claim
Population mean
The mean monthly cell phone bill in this city is μ = $42
Population proportion
Example: The proportion of adults in this city with cell phones is π = 0.68
States the claim or assertion to be tested
Is always about a population parameter, not about a sample statistic
Is the opposite of the null hypothesis
e.g., The average diameter of a manufactured bolt is not equal to 30mm ( H1: μ ≠ 30 )
Challenges the status quo
Alternative never contains the “=”sign
May or may not be proven
Is generally the hypothesis that the researcher is trying to prove
Is the opposite of the null hypothesis
e.g., The average diameter of a manufactured bolt is not equal to 30mm ( H1: μ ≠ 30 )
Challenges the status quo
Alternative never contains the “=”sign
May or may not be proven
Is generally the hypothesis that the researcher is trying to prove
Is the opposite of the null hypothesis
e.g., The average diameter of a manufactured bolt is not equal to 30mm ( H1: μ ≠ 30 )
Challenges the status quo
Alternative never contains the “=”sign
May or may not be proven
Is generally the hypothesis that the researcher is trying to prove
If the sample mean is close to the stated population mean, the null hypothesis is not rejected.
If the sample mean is far from the stated population mean, the null hypothesis...

...APP6JMaloney problems 2. 4, 6, 10, 18, 22, 24
2 ) The value of the z score un a hypothesis test is influenced by a variety of factors.
Assuming that all the other variables are held constant, explain how the value
of Z is influenced by each of the following?
Z= M - u / SD
a) Increasing the difference between the sample mean and the original.
The z score represents the distance of each X or score from the mean.
If the distance between the sample mean and the population mean the z score will
increase.
b) Increasing the population standard deviation.
The standard deviation is the factor that is used to divide by in the equation. the bigger the SD,
then the smaller the z score.
c) Increasing the number of scores in the sample.
Should bring the samples mean closer to the population mean so z score will get smaller.
4) If the alpha level is changed from .05 to .01
a) what happens to the boundaries for the critical region?
It reduces the power of the test to prove the hypothesis.
You increase the chance of rejecting a true H
b) what happens to the probability of a type 1 error?
Type 1 error is falsely reporting a hypothesis,
Where you increase the chance that you will reject a true null hypothesis.
6) A researcher is investigating the effectiveness of a new study skills training program for elementary
school childreen. A sample of n=25 third grade children is selected to...

...Hypothesistesting
Use of hypothesistesting can be very useful during decision-making connected with statistical data. A hypothesis is a statement made about a population parameter e.g. a mean and variance of a population. Hypothesistesting is a statistical process, which gives ideas or theories and then determine whether these ideas are true or false. The conclusions inhypothesistesting never 100%, therefore all tested ideas can be only probably true or probably false.
One of the most important concepts in hypothesistesting is sampling distribution.
Sampling distribution is a probability distribution of sample statistics based on all possible random samples. We have to choose randomly some amount of samples to conduct testing. The more samples size we take the better our sample curve looks normally distributed. Difference between the sample mean and population mean is a sampling error. The less this error the better result of testing. Usually we take 30 samples, which are enough to draw normally distributed curve.
Typical use scenario below will make clear the real life situation when we may use Hypothesistesting:
A bottled water manufacturer states on the product label that each of bottle contains 500 ml of water. We work for the government agency...