Class Notes (887,128)
US (344,409)
UND (454)
PSYC (84)
PSYC 241 (7)
Lecture 8

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

5 Pages
87 Views

Department
Psychology
Course Code
PSYC 241
Professor
Kristen Leighton

This preview shows pages 1 and half of page 2. Sign up to view the full 5 pages of the document.

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
Subscribers Only

Only pages 1 and half of page 2 are available for preview. Some parts have been intentionally blurred.

Unlock Document
Subscribers Only
You're Reading a Preview

Unlock to view full version

Unlock Document
Subscribers Only

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