Textbook Notes (368,128)
Canada (161,665)
Psychology (3,337)
PSYC 2360 (100)
Chapter 8

CHAPTER 8 NOTES.pdf

7 Pages
131 Views
Unlock Document

Department
Psychology
Course
PSYC 2360
Professor
Mark Fenske
Semester
Winter

Description
Chapter 8: Hypothesis Testing and Inferential Statistics Probability and Inferential Statistics - any pattern of data that might have been caused by a true relationship between variables might instead have been caused by chance - why research never actually “proves” hypothesis or theory - Hypothesis Testing Flow Chart: Develop Research Hypothesis Set alpha (Usually α=.05) Calculate power to determine sample size that is needed Collect Data Calculate statistic and p-value Compare p-value to alpha(.05) p < .05 p > .05 Reject Fail to Reject Null Hypothesis Null Hypothesis - Those previous procedures involve - use of probability - statistical analysis - Inferential Statistics -- statistical procedures that use sample data to draw inferences - about true state of affaires Sampling Distributions and Hypothesis Testing - directly testing whether a research hypothesis is correct or incorrect NOT achievable goal - not possible to specify what observed data would look like if hypothesis was true - possible to specify in statistical sense, what observed data would look like if hypothesis was not true - Sampling Distribution -- distribution of all the possible values of a statistic - each statistic has associated sampling distribution - there is a sampling distribution for: - mean - standard deviation - correlation coefficient - Binomial Distribution -- distribution of correct and incorrect guesses - as sample size ↑, extreme values are less likely to be observed - as sample size ↑, distribution becomes narrower - Null Hypothesis - Null Hypothesis (H ) 0- assumption that the observed data reflects only what - would be expected under the sampling distribution - specifies the least-interesting possible outcome - the hope in an experiment is to REJECT the null hypothesis - to be able to conclude that observed data was caused by something other than chance - Testing for Statistical Significance - Setting Alpha - observed data must deviate substantially from what is to be expected in order to reject null hypothesis - Significance Level (alpha) -- standard that the observed data must - meet - alpha normally = 0.05 - rejecting null hypothesis if observed data = so unusual that they would have occurred by chance at max. 5% of the time - as alpha ↓, ↑ stringent the standard - Comparing p-value to Alpha - Probability Value (p-value) -- shows likelihood of an observed statistic - occurring on basis of sampling distribution - indicates how extreme data scores are in terms of caused by chance - Statistically Significant -- if the p-value is less than alpha (p < .05) - REJECT null hypothesis - Statistically Nonsignificant -- if the p-value is greater than alpha - (p > .05) - FAIL TO REJECT null hypothesis - p-value for given outcome is found through examination of sampling distribution of statistic - Using One- and Two-Sided p-values - One-sided p-values -- unusual outcomes occur in only one way - Two-sided p-values -- unusual outcome occur in more than one way - p-value is always 2x bigger than one-way p- value - because binomial distribution is symmetrical - provide more conservative statistical test - allow us to interpret statistically significant relationships - even if differences are not in direction originally predicted in hypothesis Reduction of Inferential Errors REJECT FAIL TO REJECT Null Hypothesis Null Hypothesis Null Hypothesis is Type 1 Error Correct Decision TRUE Probability = α Probability = 1 - α Null Hypothesis is Correct Decision Type 2 Error FALSE Probability = 1- β Probability = β **POWER** - Type 1 Errors - Type 1 Error -- reject null hypothesis when it was actually true - should have failed to reject null hypothesis - we know we will make a Type 1 error no more than 5% of the time (if α = .05) - you never know for sure whether or not you make a Type 1 error - it is possible that data that interpreted as rejecting null hypothesis are cause by random error and that the null hypothesis is really true - by setting alpha, it allows us to assure that a Type 1 error has not been made - Type 2 Errors - Type 2 Error -- fail to reject null hypothesis when it was actually false - should have rejected null hypothesis ▯ - missing true relationship - Statistical Power - Power -- probability that the research will reject the null hypothesis given that - the null hypothesis is actually false - correctly rejecting null hypothesis - Power = 1 - β - depends in part of how big the relationship being searched for actually is - bigger it is, easier to detect - Effect Size - beta can only be estimated - Effect Size -- size of relationship between variables - indicated by a statistic - indicates the magnitude of relationship - 0 = no relationship between variables - large & positive = strong relationship between variables - because researcher can never know ahead of time the exact effect size of relationship - cannot exactly calculate power
More Less

Related notes for PSYC 2360

Log In


OR

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