The five-step processes for hypothesis testing are the following.

Step1. Specify the null hypothesis H0 and alternative hypothesis H1. The null hypothesis is the hypothesis that the researcher formulates and proceeds to test. If the null hypothesis is rejected after the test, the hypothesis to be accepted is called the alternative hypothesis. For example if the researcher wants to compare the average value generated by two different procedures the null hypothesis to be tested is [pic] and the alternative hypothesis is [pic]

Step2. Specify the significance level ((). The significance level of a test is the probability of rejecting a null hypothesis when it is true. A test is to be constructed in such a manner that the probability of committing this error (Type I error) a pre assigned value.

Step3. Specify the test statistic and its sampling distribution. The decision to accept or reject a null hypothesis is to be based on the sample values. But, the sample values as such cannot be used for this purpose. Hence, we use a statistic, called test statistic, for this purpose. Based on the value of this test statistic we decide either to reject or accept the null hypothesis.

Step4. The fourth step is to calculate the probability value (often called the p value). The p-value is the probability of obtaining the test value as large as the observed value, when the null hypothesis was really true. Thus it can be interpreted as the probability of wrongly rejecting the null hypothesis. This is to be calculated by using the sampling distribution of the test statistic.

Step5. The p value computed in Step 4 is compared with the significance level chosen in Step 2. If the p value is less than or equal to the significance level, then the null hypothesis is rejected; if the probability is greater than the significance level then the null hypothesis is not rejected. When the null hypothesis is rejected, the outcome is said to be...

...all, the video did a fair job buttressing my understanding of hypothesistesting. The textbook explained the aspects and steps of hypothesistesting in a legible fashion, while the video helped demonstrate a real-life application.
I learned from the text that hypothesistesting 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 hypothesistesting 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...

...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...

...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...

...CHAPTER
8
Introduction to
HypothesisTesting
8.1
Inferential Statistics
and HypothesisTesting
LEARNING OBJECTIVES
8.2 Four Steps to
HypothesisTesting
After reading this chapter, you should be able to:
8.3
HypothesisTesting and
Sampling Distributions
8.4
Making a Decision:
Types of Error
8.5
Testing a Research
Hypothesis: Examples
Using the z Test
8.6
Research in Focus:
Directional Versus
Nondirectional Tests
8.7
Measuring the Size of
an Effect: Cohen’s d
8.8
Effect Size, Power, and
Sample Size
8.9
Additional Factors That
Increase Power
1 Identify the four steps of hypothesistesting.
2 Define null hypothesis, alternative hypothesis,
level of significance, test statistic, p value, and
statistical significance.
3 Define Type I error and Type II error, and identify the
type of error that researchers control.
4 Calculate the one-independent sample z test and
interpret the results.
5 Distinguish between a one-tailed and two-tailed test,
and explain why a Type III error is possible only with
one-tailed tests.
6 Explain what effect size measures and compute a
Cohen’s d for the one-independent sample z test.
7 Define power and...

...5StepHypothesis for Regression
Team D will conduct a test on the hypotheses :
H₀: M₁ ≤ M₂
The null hypothesis states that non-European Union countries (M₁) have a lesser/equal to life expectancy than European Union countries (M₂).
H₁: M₁ > M₂
The alternative hypothesis states that non-European Union (M₁) countries have a greater life expectancy than European Union countries (M₂).
Team D will conduct research with a level of significance of α = .05
Identify the test statistic: Team D will use the results from our regression analysis to determine whether the slope of the regression line differs significantly from zero.
The decision rule is with 95% confidence interval of the 63 sample observations the rejection of the null hypothesis if the computation of the p-value is greater than 2.87 and if the p-value is less than 2.87 the null hypothesis is accepted.
Regression Analysis | | | | | |
| | | | | | |
| r² | 0.076 | n | 62 | | |
| r | -0.276 | k | 1 | | |
| Std. Error | 10.734 | Dep. Var. | Literacy % | |
| | | | | | |
ANOVA table | | | | | | |
Source | SS | df | MS | F | p-value | |
Regression | 569.73600489 | 1 | 569.73600489 | 4.94 | .0299 | |
Residual | 6,913.25576931 | 60 | 115.22092949 | | | |
Total | 7,482.99177419 | 61 | | | | |
| | | | | | |...

...What is HypothesisTesting?
A statistical hypothesis is an assumption about a population parameter. This assumption may or may not be true. Hypothesistesting refers to the formal procedures used by statisticians to accept or reject statistical hypotheses.
Statistical Hypotheses
Null hypothesis. The null hypothesis, denoted by H0, is usually the hypothesis that sample observations result purely from chance.
Alternative hypothesis. The alternative hypothesis, denoted by H1 or Ha, is the hypothesis that sample observations are influenced by some non-random cause.
Hypothesis Tests
Statisticians follow a formal process to determine whether to reject a null hypothesis, based on sample data. This process, called hypothesistesting, consists of four steps.
State the hypotheses. This involves stating the null and alternative hypotheses. The hypotheses are stated in such a way that they are mutually exclusive. That is, if one is true, the other must be false.
Formulate an analysis plan. The analysis plan describes how to use sample data to evaluate the null hypothesis. The evaluation often focuses around a single test statistic.
Analyze sample data. Find the value of the test statistic (mean score, proportion, t-score, z-score, etc.)...

...HYPOTHESISTESTING
WHAT IS THIS HYPOTHESIS????
• In simple words it means a mere assumption or supposition to be proved of disproved.
• But, for a researcher it is a formal question that he intends to resolve.
• Example: I assume that 1) under stress and anxiety a person goes into depression.
2) It leads to aggressive behaviour.
Eg. : Students who get better counselling in a university will show a greater increase in creativity than students who were not counselled.
• So, the hypothesis should be capable of being verified and tested.
CHARACTERISTICS
• Should be clear and precise – inferences not reliable
• Capable of being tested
“ A hypothesis is testable if other deductions can be made from it which, in turn can be confirmed or disproved by observation.”
• Should be limited in scope and must be specific
• Should be stated in simple terms -understandable by all concerned.
• Must explain the facts that gave rise to the need for explanation.
BASIC CONCEPTS: NULL & ALTERNATIVE HYPOTHESIS
• If we are to compare two methods A & B and both are equally good, then this assumption is termed as null hypothesis(H0)
• If it is stated that method A is better than method B-alternative hypothesis(Ha)
LEVEL OF SIGNIFICANCE
• A very important concept in the context of hypothesistesting
• It is represented in a % age...

...Hypothesistesting begins with the assumption that randomization is used to collect quantitative data about the sample and that the distribution of this data has a normal shape.
Significance tests state two explanations, or hypotheses, about a parameter. One, called the null hypothesis, states that the parameter equals some value (usually 0). The other, called the alternative hypothesis, states that the parameter is greater than, less than or (not) equal to the value stated in the null hypothesis. When the alternative hypothesis considers the values above and below the null hypothesis, it is called two sided.
Statistical significance is not about the importance of a hypothesis, but rather the likelihood that an observation would occur if the null hypothesis is true. The P-value is the probability that an estimate deviates from the value stated in the alternative hypothesis, assuming that the null hypothesis is true. Statistical significance is commonly stated when P-values are less than .05, or 5%. When this occurs, this means that there is more evidence against the null hypothesis. One common misstep is to accept the null hypothesis because there is not sufficient evidence to reject it. There are also 2 types of errors that researchers can make. The first, called Type 1...