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Basic Principle
Probability, Samples & Populations
Examine the probability that the outcome of a study might be possible even if the true situation was that the experimental manipulation made no different (i.e., had no effect).
Probability
Cannot
Basic Principle Probability, Samples & Populations
Examine the probability that the outcome of a study might be possible even if the true situation was that the experimental manipulation made no different (i.e., had no effect).
Probability
Cannot
Linear Regression
"I always avoid prophesying beforehand because it is a much better policy to prophesy after the event has already taken place." Winston Churchill
Prediction
If you want to predict a person's score on a given measurement, what
Linear Regression
"I always avoid prophesying beforehand because it is a much better policy to prophesy after the event has already taken place." - Winston Churchill
Prediction
Regression is about improving our prediction of scores Regression uses
Overview: Correlation
Visualizing relationships: the scatterplot Two methods of obtaining a measure of correlation:
Using covariance Using z-scores
Correlation
relationships among variables
Assumptions, concerns, and limitations
First approac
Lecture Outline
The Normal Distribution The Standard Normal Distribution and ZScores
Comparing a score to the population
The Normal Distribution
A Normal Distribution: Example
The Normal Distribution
Unimodal & Continuous (from to + infini
Lecture Outline
Measures of Central Tendency Measures of Variability Visualizing Data Reporting Descriptive Statistics
Descriptive Statistics
Visualizing your data.
Tables/Charts (go over in book) Figures/Graphs
Histograms & Bar Charts (on your own
Lecture Outline
Measures of Central Tendency Measures of Variability Visualizing Data
Descriptive Statistics
Measures of Central Tendency
1. Mean (population = ; sample = (Xbar) or M
"Average" Interval/ratio scale Usually the best descriptiv
Introduction
Still do not know the population parameters Dependent t-test: compare two groups of tscores from the "same" subjects same" Independent t-test: compares two groups of tscores from two different groups of subjects
Example: Experimental and
Steps for Hypothesis Testing
1.
Hypothesis Testing
2. 3. 4. 5. 6. 7. 8. 9.
H1 and H0 Determining the nature of the dependent variable Choosing the appropriate test statistic Setting Type I & Type II error rates Determining sample size Collecting d
Steps for Hypothesis Testing
1. H1 and H0 2. Determining the nature of the dependent variable 3. Choosing the appropriate test statistic 4. Setting Type I & Type II error rates 5. Determining sample size 6. Collecting data 7. Conducting appropriate s
Outline
Chi Square
Overview Goodness of Fit Test of Association/Independence
Nonparametric Statistics
Non-parametric statistics: Non No assumptions about the shape of the population.
Examples: 2, Binomial, Sign Test
Chi-Square Tests
Hypothesis te
Outline
Chi Square
Overview Goodness of Fit Test of Association/Independence
Nonparametric Statistics
Non-parametric statistics: Non No assumptions about the shape of the population.
Examples: 2, Binomial, Sign Test
Chi-Square Tests
Hypothesis te
Sampling Distributions
Z-distribution
Hypothesis Tests with Means of Samples
Introducing the Central Limit Theorem (CLT)
A comparison distribution of individual scores. We had the population and want to know how ONE score fits on that distributi
Outline One Sample Z-Test
1-Sample Z-Test
Using hypothesis testing to determine significance.
Estimation
Using confidence intervals to determine significance.
Estimation
One Sample Z-Test
Second inferential Test Given the CLT, Z can be used
Outline One Sample Z-Test
1-Sample Z-Test
Using hypothesis testing to determine significance.
Estimation
Using confidence intervals to determine significance.
Estimation
One Sample Z-Test
Second inferential Test Given the CLT, Z can be used
Steps for Hypothesis Testing
1. H1 and H0 2. Determining the nature of the dependent variable 3. Choosing the appropriate test statistic 4. Setting Type I & Type II error rates 5. Determining sample size 6. Collecting data 7. Conducting appropriate s
Statistics 031: Spring 2008
Syllabus: Statistics for the Behavioral Sciences PSY 031 Spring 2008
Instructor: Jennifer DiCorcia Dept. of Psychology, Rm #329 jennifer.dicorcia@tufts.edu Office Hrs: Mondays 12-1pm & by appt. Lab Instructors: Steve Mahe
Steps for Hypothesis Testing
1. 2. 3. 4. 5. 6. 7. 8. 9. H1 and H0 Determining the nature of the dependent variable Choosing the appropriate test statistic Setting Type I & Type II error rates Determining sample size Collecting data Conducting appropr
Steps for Hypothesis Testing
1. H1 and H0 2. Determining the nature of the dependent variable 3. Choosing the appropriate test statistic 4. Setting Type I & Type II error rates 5. Determining sample size 6. Collecting data 7. Conducting appropriate s
Steps for Hypothesis Testing
1.
Hypothesis Testing
2. 3. 4. 5. 6. 7. 8. 9.
H1 and H0 Determining the nature of the dependent variable Choosing the appropriate test statistic Setting Type I & Type II error rates Determining sample size Collecting d
Introduction
Still do not know the population parameters Dependent t-test: compare two groups of scores from the "same" subjects Independent t-test: compares two groups of scores from two different groups of subjects
Example: Experimental and con
Steps for Hypothesis Testing
1. H1 and H0 2. Determining the nature of the dependent variable 3. Choosing the appropriate test statistic 4. Setting Type I & Type II error rates 5. Determining sample size 6. Collecting data 7. Conducting appropriate s
Introduction
Rare that you know the population mean
1 Sample t-Test
Dependent t-Test
More likely comparing samples:
1. Dependent t-Test
Within-Subjects t-Test Repeated Measures t-Test
2. Independent t-Test
Between Subjects t-Test
Dependent
Test Guide
2 Groups/ Conditions Do independent groups differ on one mean? Does one group differ across repeated measures? > 2 Groups/ Conditions
Test Guide
One Way.
One factor (i.e., one IV)
Independent/ between One-way ANOVA subjects t-test Pair
Outline
Introduction to the t-distribution tNormal distribution vs. t-distribution t1 Sample t-test tExample
The tDistribution
Introduction
When do we ever know the standard deviation of the population ()? (
Can use M when testing hypotheses about
Looking ahead.
Week
Looking ahead.
Week
of 3/24 Lab #7 Week of 3/31 Lab #8 Week of 4/7 Lab #9 Week of 4/14 Game Show in Labs
If there is room, you're more than welcome to you' sit-in on other labs for review! sit Quiz
of 4/21 Lab #10 4/23 La