Statistics: Unlocking the Power of Data Lock 5 Section 6.12 Test for a Difference in Means.

Slides:



Advertisements
Similar presentations
Chapter 12: Testing hypotheses about single means (z and t) Example: Suppose you have the hypothesis that UW undergrads have higher than the average IQ.
Advertisements

8.3 T- TEST FOR A MEAN. T- TEST The t test is a statistical test for the mean of a population and is used when the population is normally or approximately.
© 2013 Pearson Education, Inc. Active Learning Lecture Slides For use with Classroom Response Systems Introductory Statistics: Exploring the World through.
Inferential Statistics & Hypothesis Testing
Statistics: Unlocking the Power of Data Lock 5 STAT 101 Dr. Kari Lock Morgan 10/25/12 Sections , Single Mean t-distribution (6.4) Intervals.
Single Sample t-test Purpose: Compare a sample mean to a hypothesized population mean. Design: One group.
PSY 307 – Statistics for the Behavioral Sciences
Testing the Difference Between Means (Small Independent Samples)
Slide 4- 1 Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Active Learning Lecture Slides For use with Classroom Response.
AP Statistics Section 12.1 A. Now that we have looked at the principles of testing claims, we proceed to practice. We begin by dropping the unrealistic.
Chapter 9 Hypothesis Testing.
Statistics for the Social Sciences Psychology 340 Spring 2005 Using t-tests.
Hypothesis Testing Using The One-Sample t-Test
Getting Started with Hypothesis Testing The Single Sample.
Confidence Intervals for the Mean (σ Unknown) (Small Samples)
AM Recitation 2/10/11.
Hypothesis Testing:.
Hypothesis Testing with Two Samples
8 - 1 © 2003 Pearson Prentice Hall Chi-Square (  2 ) Test of Variance.
Comparing means Norhafizah Ab Manan. After class, you should Understand independent t test, paired t test and ANOVA Know how to calculate the t statistics.
Copyright © 2015, 2012, and 2009 Pearson Education, Inc. 1 Section 7.3 Hypothesis Testing for the Mean (  Unknown).
Normal Distribution Chapter 5 Normal distribution
t-Test: Statistical Analysis
Sullivan – Fundamentals of Statistics – 2 nd Edition – Chapter 11 Section 2 – Slide 1 of 25 Chapter 11 Section 2 Inference about Two Means: Independent.
T-distribution & comparison of means Z as test statistic Use a Z-statistic only if you know the population standard deviation (σ). Z-statistic converts.
7 Elementary Statistics Hypothesis Testing. Introduction to Hypothesis Testing Section 7.1.
X _ X _ X _ X _ X _ X _ X _ X _ X _ X _ X _ X _ X _ X _ X _ X _ X _ X _ X _ X _ X _ X _ X _ X _ X _ X _ X _ X _ X _ X _ X _ X _ X _ X _ X _ X _ μ.
Hypothesis Testing with One Sample Chapter 7. § 7.3 Hypothesis Testing for the Mean (Small Samples)
Inference for Quantitative Variables 3/12/12 Single Mean, µ t-distribution Intervals and tests Difference in means, µ 1 – µ 2 Distribution Matched pairs.
Chapter 12 Tests of a Single Mean When σ is Unknown.
1 Section 9-4 Two Means: Matched Pairs In this section we deal with dependent samples. In other words, there is some relationship between the two samples.
Section 8.3 Testing the Difference Between Means (Dependent Samples)
DIRECTIONAL HYPOTHESIS The 1-tailed test: –Instead of dividing alpha by 2, you are looking for unlikely outcomes on only 1 side of the distribution –No.
Hypothesis Testing for the Mean (Small Samples)
Section 8-5 Testing a Claim about a Mean: σ Not Known.
Section Copyright © 2014, 2012, 2010 Pearson Education, Inc. Lecture Slides Elementary Statistics Twelfth Edition and the Triola Statistics Series.
Unit 8 Section 8-3 – Day : P-Value Method for Hypothesis Testing  Instead of giving an α value, some statistical situations might alternatively.
Chapter 8 Parameter Estimates and Hypothesis Testing.
Aim: How do we use a t-test?
The t-distribution William Gosset lived from 1876 to 1937 Gosset invented the t -test to handle small samples for quality control in brewing. He wrote.
Statistics: Unlocking the Power of Data Lock 5 Section 4.2 Measuring Evidence with p-values.
Statistics: Unlocking the Power of Data Lock 5 Inference for Means STAT 250 Dr. Kari Lock Morgan Sections 6.4, 6.5, 6.6, 6.10, 6.11, 6.12, 6.13 t-distribution.
Confidence Intervals for a Population Mean, Standard Deviation Unknown.
Section Copyright © 2014, 2012, 2010 Pearson Education, Inc. Lecture Slides Elementary Statistics Twelfth Edition and the Triola Statistics Series.
_ z = X -  XX - Wow! We can use the z-distribution to test a hypothesis.
AP Statistics.  If our data comes from a simple random sample (SRS) and the sample size is sufficiently large, then we know that the sampling distribution.
Statistics: Unlocking the Power of Data Lock 5 Section 6.4 Distribution of a Sample Mean.
Copyright © 2015, 2012, and 2009 Pearson Education, Inc. 1 Section 6.2 Confidence Intervals for the Mean (  Unknown)
Statistics: Unlocking the Power of Data Lock 5 Inference for Means STAT 250 Dr. Kari Lock Morgan Sections 6.4, 6.5, 6.6, 6.10, 6.11, 6.12, 6.13 t-distribution.
Section 6.2 Confidence Intervals for the Mean (Small Samples) Larson/Farber 4th ed.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.Copyright © 2010 Pearson Education Section 9-4 Inferences from Matched.
Statistics: Unlocking the Power of Data Lock 5 Section 6.3 Test for a Single Proportion.
Chapter 7 Inference Concerning Populations (Numeric Responses)
Two Sample Problems  Compare the responses of two treatments or compare the characteristics of 2 populations  Separate samples from each population.
Unit 8 Section : Hypothesis Testing for the Mean (σ unknown)  The hypothesis test for a mean when the population standard deviation is unknown.
Section 6.2 Confidence Intervals for the Mean (Small Samples) © 2012 Pearson Education, Inc. All rights reserved. 1 of 83.
Statistics: Unlocking the Power of Data Lock 5 Section 6.11 Confidence Interval for a Difference in Means.
Chapter 7 Statistics Power Point Review Hypothesis Testing.
Testing Claims about a Population Mean Objective: test a claim.
Ex St 801 Statistical Methods Part 2 Inference about a Single Population Mean (HYP)
Statistics: Unlocking the Power of Data Lock 5 Section 6.6 Test for a Single Mean.
Hypothesis Testing – Two Means(Small, Independent Samples)
Hypothesis Testing for Means (Small Samples)
Chapter 8 Hypothesis Testing with Two Samples.
Distribution of a Difference in Means
Elementary Statistics
Elementary Statistics: Picturing The World
A paired-samples t-test compares the means of two related sets of data to see if they differ statistically. IQ Example We may want to compare the IQ scores.
Elementary Statistics: Picturing The World
Presentation transcript:

Statistics: Unlocking the Power of Data Lock 5 Section 6.12 Test for a Difference in Means

Statistics: Unlocking the Power of Data Lock 5 Outline Test for a difference in means

Statistics: Unlocking the Power of Data Lock 5 T-Test for a Difference in Means If the population is approximately normal or if sample sizes are large (n 1 ≥ 30, n 2 ≥ 30), the p- value can be computed as the area in the tail(s) beyond t of a t-distribution with degrees of freedom equal to the smaller of n 1 – 1 and n 2 – 1 H0:μ1=μ2H0:μ1=μ2

Statistics: Unlocking the Power of Data Lock 5 The Pygmalion Effect Source: Rosenthal, R. and Jacobsen, L. (1968). “Pygmalion in the Classroom: Teacher Expectation and Pupils’ Intellectual Development.” Holt, Rinehart and Winston, Inc. Pygmalion Effect: the greater the expectation placed upon people, the better they perform Teachers were told that certain children (chosen randomly) were expected to be “growth spurters,” based on the Harvard Test of Inflected Acquisition (a test that didn’t actually exist). The response variable is change in IQ over the course of one year.

Statistics: Unlocking the Power of Data Lock 5 The Pygmalion Effect ns Control Students “Growth Spurters” Does this provide evidence that the Pygmalion Effect exists? (that merely expecting a child to do better actually causes the child to do better?) (a) Yes (b) No *s 1 and s 2 were not given, so I set them to give the correct p-value

Statistics: Unlocking the Power of Data Lock 5 Pygmalion Effect We have evidence that positive teacher expectations significantly increase IQ scores, on average, in elementary school children. 1. State hypotheses: 2. Check conditions: 3. Calculate statistic: 5. Calculate test statistic: 7. Interpret in context: t with 65 – 1 = 64 df, upper tail p-value = Compute p-value: H 0 :  1 =  2 H a :  1 >  2  1 = average change in IQ for “growth spurters”  1 = average change in IQ for control n 1 = 65 ≥ 30, n 2 = 255 ≥ Calculate standard error:

Statistics: Unlocking the Power of Data Lock 5 Pygmalion Effect From the paper: “The difference in gains could be ascribed to chance about 2 in 100 times”