Class Notes (944,784)
US (365,161)
UND (454)
PSYC (84)
PSYC 241 (7)
Lecture 8

PSYC 241 Lecture 8: Lesson 8: Introduction to Hypothesis Testing
Premium

by OneClass1169246 , Spring 2017
5 Pages
87 Views

Department
Psychology
Course Code
PSYC 241
Professor
Kristen Leighton
Lecture
8

This preview shows pages 1-2. Sign up to view the full 5 pages of the document.
Lesson 8: Introduction to Hypothesis Testing
Hypothesis Testing
Goal: rule out chance (sampling error) as a plausible explanation for results
Hypothesis testing helps determine whether a treatment has an effect
To evaluate research in which:
1. A sample is selected from the population
2. The treatment is administered to the sample
3. The people in the sample are measured after treatment
If people in sample are noticeably different from those in original population, we have
evidence of treatment effect
It is also possible, however, that the difference between sample and population is due
to sampling error
A statistical hypothesis is an assumption about a population parameter
Best way to determine whether hypothesis is true would be to examine entire
population
Impractical --> impossible
Random sample is proxy for population
Use this sample to test hypothesis
Two possible explanations:
1. The difference between sample and population can be explained by sampling
error
No treatment effect
2. The difference is too large to be due to sampling error
Treatment effect
Cannot "prove" things to be true
Much easier to show that something is false
Dilemma for researchers:
We want to support our hypotheses, but the techniques available are better for showing
that something is false
Null Hypothesis
Two types of statistical hypotheses:
1. Null hypothesis: No difference
H0
No difference between sample statistics and population parameters or
No difference between samples
--> no difference between populations
Any minor differences due to chance
Alternative Hypothesis
2. Alternative hypothesis: Difference
H1 or Ha
find more resources at oneclass.com
find more resources at oneclass.com
Difference between sample statistics and population parameter or
Difference between two samples
--> difference between populations
Difference due to some non-random cause
Even though we are using samples to test our hypothesis, the hypotheses are always stated
based on populations
Statistical Notation
H0: µ0 = µ1
No difference in population means
H1: µ0 doesn't equal µ1
Ha: µ0 doesn't equal µ1
Population means are not equal (two-tailed, non-directional)
Non-directional tests are more stringent; more conservative
Directional Tests
If researchers predicts a specific direction for the treatment effect (increase or
decrease), can incorporate the directional prediction into the hypothesis test
The result is called a directional test or a one-tail test
Directional test includes directional prediction in statement of hypotheses and location
of the critical region
For example, if the original population has a mean of 80 and treatment is predicted to
increase the scores, the null hypothesis would state that after treatment:
H0: mean is equal to or < 80 (there is no increase)
Statistical Notation
H1: µ0 < µ1
H1: µ0 > µ1
Ha: µ0 < µ1
Ha: µ0 > µ1
One mean is greater (or less than) the other (one-tailed, directional)
The Null hypothesis, the Alpha Level, the Critical Region, and the Test Statistic (can be written on
calculation sheet)
5 steps in hypothesis testing:
Step 1: State Hypothesis
State the hypotheses; select an α (alpha) level
Null hypothesis, H0, always states that the treatment has no effect
Alpha level establishes a criterion, or "cut-off," for making a decision
about the null hypothesis
find more resources at oneclass.com
find more resources at oneclass.com

Loved by over 2.2 million students

Over 90% improved by at least one letter grade.

Leah — University of Toronto

OneClass has been such a huge help in my studies at UofT especially since I am a transfer student. OneClass is the study buddy I never had before and definitely gives me the extra push to get from a B to an A!

Leah — University of Toronto
Saarim — University of Michigan

Balancing social life With academics can be difficult, that is why I'm so glad that OneClass is out there where I can find the top notes for all of my classes. Now I can be the all-star student I want to be.

Saarim — University of Michigan
Jenna — University of Wisconsin

As a college student living on a college budget, I love how easy it is to earn gift cards just by submitting my notes.

Jenna — University of Wisconsin
Anne — University of California

OneClass has allowed me to catch up with my most difficult course! #lifesaver

Anne — University of California
Description
find more resources at oneclass.com Lesson 8: Introduction to Hypothesis Testing ● Hypothesis Testing ○ Goal: rule out chance (sampling error) as a plausible explanation for results ○ Hypothesis testing helps determine whether a treatment has an effect ○ To evaluate research in which: ■ 1. A sample is selected from the population ■ 2. The treatment is administered to the sample ■ 3. The people in the sample are measured after treatment ○ If people in sample are noticeably different from those in original population, we have evidence of treatment effect ○ It is also possible, however, that the difference between sample and population is due to sampling error ○ A statistical hypothesis is an assumption about a population parameter ○ Best way to determine whether hypothesis is true would be to examine entire population ■ Impractical --> impossible ○ Random sample is proxy for population ○ Use this sample to test hypothesis ○ Two possible explanations: ■ 1. The difference between sample and population can be explained by sampling error ■ No treatment effect ■ 2. The difference is too large to be due to sampling error ■ Treatment effect ● Cannot "prove" things to be true ● Much easier to show that something is false ● Dilemma for researchers: ○ We want to support our hypotheses, but the techniques available are better for showing that something is false ● Null Hypothesis ○ Two types of statistical hypotheses: ■ 1. Null hypothesis: No difference ■ H0 ■ No difference between sample statistics and population parameters or ■ No difference between samples --> no difference between populations ● Any minor differences due to chance ● Alternative Hypothesis ○ 2. Alternative hypothesis: Difference ■ H1or Ha find more resources at oneclass.com find more resources at oneclass.com ■ Difference between sample statistics and population parameter or ■ Difference between two samples --> difference between populations ● Difference due to some non-random cause ● Even though we are using samples to test our hypothesis, the hypotheses are always stated based on populations ● Statistical Notation ○ H 0 µ0= µ1 ■ No difference in population means ○ H : µ doesn't equal µ 1 0 1 ○ H a µ0doesn't equal1µ ■ Population means are not equal (two-tailed, non-directional) ■ Non-directional tests are more stringent; more conservative ● Directional Tests ○ If researchers predicts a specific direction for the treatment effect (increase or decrease), can incorporate the directional prediction into the hypothesis test ○ The result is called a directional test or a one-tail test ○ Directional test includes directional prediction in statement of hypotheses and location of the critical region ○ For example, if the original population has a mean of 80 and treatment is predicted to increase the scores, the null hypothesis would state that after treatment: ■ H0: mean is equal to or < 80 (there is no increase) ● Statistical Notation ○ H : µ < µ 1 0 1 ○ H 1 µ0> µ1 ○ H a µ0< µ1 ○ H a µ0> µ1 ■ One mean is greater (or less than) the other (one-tailed, directional) ● The Null hypothesis, the Alpha Level, the Critical Region, and the Test Statistic (can be written on calculation sheet) ○ 5 steps in hypothesis testing: ■ Step 1: State Hypothesis ■ State the hypotheses; select an α (alpha) level ■ Null hypothesis0 H , always states that the treatment has no effect ■ Alpha level establishes a criterion, or "cut-off," for making a decision about the null hypothesis find more resources at oneclass.com find more resources at oneclass.com ■ Alpha level also determines risk of Type I error ■ Step 2: Set Criteria ■ Locate the critical region ■ The critical region consists of outcomes that are very unlikely to occur if the null hypothesis is true ■ Means that are almost impossible to obtain if the treatment has no effect ■ "almost impossible" means probability (p) that is less than the alpha level ■ Step 3: Collect and analyze data
More Less
Unlock Document


Only pages 1-2 are available for preview. Some parts have been intentionally blurred.

Unlock Document
You're Reading a Preview

Unlock to view full version

Unlock Document

Log In


OR

Don't have an account?

Join OneClass

Access over 10 million pages of study
documents for 1.3 million courses.

Sign up

Join to view


OR

By registering, I agree to the Terms and Privacy Policies
Already have an account?
Just a few more details

So we can recommend you notes for your school.

Reset Password

Please enter below the email address you registered with and we will send you a link to reset your password.

Add your courses

Get notes from the top students in your class.


Submit