Introduction to Hypothesis Testing. Hypothesis Testing. Step 1: State the Hypotheses


 Jemima Whitney Lambert
 7 years ago
 Views:
Transcription
1 Introduction to Hypothesis Testing 1 Hypothesis Testing A hypothesis test is a statistical procedure that uses sample data to evaluate a hypothesis about a population Hypothesis is stated in terms of the population Predict sample statistics based on population parameters (e.g. µ) Select random sample from population Compare observed sample data with predicted values 2 Step 1: State the Hypotheses The null hypothesis, H 0, states that in the population there is no change, no difference, or no relationship H 0 : µ treatment = constant (e.g. µ) e.g. H 0 : µ treatment = 100 This is read as: The null hypothesis is that the population mean of people receiving the treatment equals 100 H 0 is that the treatment had no effect 3 1
2 H 0 The null hypothesis must contain an equal sign of some sort (=,, ) Statistical tests are designed to reject H 0, never to accept it 4 H 1 : The Alternative Hypothesis The alternative hypothesis usually takes the following form: H 1 : µ treatment constant (e.g. µ) e.g. H 1 : µ treatment 100 This is read as: The alternative hypothesis states that the population mean of people receiving the treatment does not equal 100 H 1 is that the treatment had an effect 5 H 0 and H 1 Together, the null and alternative hypotheses must be mutually exclusive and exhaustive Mutual exclusion implies that H 0 and H 1 cannot both be true at the same time Exhaustive implies that each of the possible outcomes of the experiment must make either H 0 or H 1 true 6 2
3 Step 2: Set the Decision Criteria What sample means are consistent with H 0 and what sample means are consistent with H 1? Separate distribution of sample means into two sets of regions one whose means are consistent with H 0 and one whose means are consistent with H 1 n = 25, µ = 100, σ = 15 for graph Extreme, lowprobability values if H 0 is true Sample means close to H 0 : highprobability values if H 0 is true Extreme, lowprobability values if H 0 is true 7 α Level The α level (alpha level; level of significance) is a probability value that is used to define the very unlikely sample outcomes if H 0 is true Psychologists usually adopt α = 0.05, although α = 0.01 and α = are sometimes used The critical region is composed of the extreme sample values that are very unlikely (as specified by the α level) to be obtained if H 0 is true 8 Since we can reject H 0 two ways (extremely small or extremely large sample means), the α level is divided across the two tails of the distribution Find the zscore whose area above equals α / 2 z = 1.96 for α = 0.05 Find raw scores that Critical Regions Extreme, lowprobability values if H 0 is true, z = Sample means close to H 0 : highprobability values if H 0 is true Extreme, lowprobability values if H 0 is true, z = 1.96 correspond to that z score X = = X = =
4 Step 3: Collect Data & Compute Sample Statistics Randomly sample from population In this example, n = 25 Give the sample the treatment Measure the dependent variable Calculate the z score of sample mean in the sampling distribution In this example the sample statistics are, = 107, s = 14; population parameters from slide 7 (IQs) 10 Step 4: Make a Decision If the sample mean s z score is in the extreme tails of the sampling distribution (e.g. in the critical region), reject H 0 ; otherwise, fail to reject H 0 Critical region is z > 1.96 or z < for α = 0.05 The example z is It is in the critical region. Therefore, reject H 0 It is likely the case that the treatment had an effect Extreme, lowprobability values if H 0 is true, z = Sample means close to H 0 : highprobability values if H 0 is true = 107; z = 2.33 Extreme, lowprobability values if H 0 is true, z = Reject H 0 or Fail to Reject H 0 The only decisions you ever make in hypothesis testing are Reject H 0. or Fail to reject H 0 No other decisions are possible Never reject H 1 Never accept H 1 Never accept H
5 Type I (α) Error A type I (or α) error occurs when a researcher rejects H 0 when H 0 is really true Researcher concludes that the treatment had an effect when it did not This should happen with a probability equal to α 13 Type II (β) Errors A type II (or β) error occurs when a researcher fails to reject H 0 when H 0 is really false Researcher concludes that there is insufficient evidence to suggest that the treatment had an effect when in fact it does have an effect This should happen with a probability equal to β 14 β Unlike α, β is not directly set by the researcher β depends on the sample size (n) β depends on how much the treatment affects the dependent variable β depends on the variability of the data β depends on α 15 5
6 TypeI and TypeII Errors Ideally, we would like to minimize both Type I and TypeII errors This is not possible for a given sample size When we lower the α level to minimize the probability of making a TypeI error, the β level will rise When we lower the β level to minimize the probability of making a TypeII error, the α level will rise 16 TypeI and TypeII Errors 17 Factors that Influence a Hypothesis Test The size of the mean difference The larger the mean difference is, the more likely you are to reject H 0 The variability of the scores The more variable the scores are, the less likely you are to reject H 0 The number of scores in the sample The larger the sample size, the more likely you are to reject H
7 Assumptions of the zscore Hypothesis Test Random sampling If the sample is not selected randomly from the population, it probably will not represent the population Independent observations σ does not change as a result of the treatment Distribution of sample means is normal 19 Directional vs NonDirectional Hypotheses The hypotheses we have been talking about are called nondirectional hypotheses because they do not specify how the population mean should differ from the constant That is, they do not say that the population mean should be larger than the constant They only state that the population mean should differ from the constant Nondirectional hypotheses are sometimes called twotailed tests 20 Directional vs NonDiretional Hypotheses Directional hypotheses include an ordinal relation between the population mean and the constant That is, they state that the population mean should be larger than the constant For directional hypotheses, the H 0 and H 1 are written as: H 0 : µ treatment constant H 1 : µ treatment > constant Directional hypotheses are sometimes called onetailed tests 21 7
8 1 Tailed When performing a one tailed test, all of the critical region is in one tail of the distribution of sample means Do not divide α by two when finding the z score for the critical region This increases statistical power the probability of correctly rejecting a false H Tailed vs. 2 Tailed 1 Tailed α=.05, z = 1.65 Critical region in one tail α=.05, z = Critical region in two tails 2 Tailed α=.05, z = 1.96 Critical region in two tails Concerns about Hypothesis Testing Hypothesis testing focuses on the data, and not the hypothesis When we reject H 0, we should really say This specific sample mean is very unlikely (p <.05) if the null hypothesis is true Statistical significance practical significance The effect size can be small, but still be statistically significant if the sample size is sufficiently large 24 8
9 Effect Size A measure of effect size is intended to provide a measurement of the absolute magnitude of a treatment effect, independent of the size of the sample(s) being used Cohen s d is a measure of effect size 25 Effect Size What is the effect size for the example on slide 5? Magnitude of d d = 0.2 d = 0.5 d = 0.8 Evaluation of Effect Size Small effect Medium effect Large effect This is a small effect 26 Statistical Power Statistical power is the probability that a statistical test will correctly reject a false H 0 Probability that a statistical test will identify a treatment effect if one really exists Power = 1 β= 1 probability of a Type II error 27 9
10 Statistical Power Calculate before performing the study Need to know / estimate How much the treatment changes the dependent variable Sample size α σ, µ 28 Statistical Power Example How much the treatment changes the dependent variable Researchers hypothesize that having proper nutrition during the first two years will increase IQ by 3 points (notice 1 tailed) µ = 100, σ = 15 Sample size n = 25 α = Distribution of Sample Means If the treatment has no effect, by the central limit theorem, the distribution of sample means will have: a mean = population mean = 100 a standard deviation = σ/ n = 15 / 25 = 3 If the treatment has the hypothesized effect, the distribution of sample means will have a mean = population mean + effect of treatment = = 103 a standard deviation = σ/ n = 15 / 25 = 3 add a constant to all scores does not change the standard deviation 30 10
11 z Score of Critical Region This is a onetailed test with α =.05 Consult a table to find the z with an area above equal to.05 z = Statistical Power Example z Statistical Power Example Power equals area to right of the z score for the critical region under the treatment distribution of sample means Areas to the right of the z score for the critical region correspond to rejecting H 0 Areas under the treatment distribution of sample means correspond to a false H 0 Both combined correspond to rejecting a false H 0 = power 33 11
12 Statistical Power Example Find the z score in the treatment distribution of sample means that is at the same location as the z score for the critical region in the no treatment distribution of sample means z treatment = z critical region z mean of treatment z mean of treatment = ( ) / 3 = 1 z treatment = = 0.65 Power = area above z = 0.65 Power =.26 Only about a 1 in 4 chance of observing this effect 34 Factors that Influence Power Sample size As sample size increases, power increases α level As α decreases (fewer Type I errors), β increases (more Type II errors), and 1 β (power) decreases Number of tails (directional vs nondirectional) One tailed tests have more statistical power than two tailed tests. Can you explain why? 35 12
HYPOTHESIS TESTING: POWER OF THE TEST
HYPOTHESIS TESTING: POWER OF THE TEST The first 6 steps of the 9step test of hypothesis are called "the test". These steps are not dependent on the observed data values. When planning a research project,
More informationSection 7.1. Introduction to Hypothesis Testing. Schrodinger s cat quantum mechanics thought experiment (1935)
Section 7.1 Introduction to Hypothesis Testing Schrodinger s cat quantum mechanics thought experiment (1935) Statistical Hypotheses A statistical hypothesis is a claim about a population. Null hypothesis
More informationChapter 8 Hypothesis Testing Chapter 8 Hypothesis Testing 81 Overview 82 Basics of Hypothesis Testing
Chapter 8 Hypothesis Testing 1 Chapter 8 Hypothesis Testing 81 Overview 82 Basics of Hypothesis Testing 83 Testing a Claim About a Proportion 85 Testing a Claim About a Mean: s Not Known 86 Testing
More informationNonParametric Tests (I)
Lecture 5: NonParametric Tests (I) KimHuat LIM lim@stats.ox.ac.uk http://www.stats.ox.ac.uk/~lim/teaching.html Slide 1 5.1 Outline (i) Overview of DistributionFree Tests (ii) Median Test for Two Independent
More informationHypothesis Testing. Reminder of Inferential Statistics. Hypothesis Testing: Introduction
Hypothesis Testing PSY 360 Introduction to Statistics for the Behavioral Sciences Reminder of Inferential Statistics All inferential statistics have the following in common: Use of some descriptive statistic
More informationLesson 9 Hypothesis Testing
Lesson 9 Hypothesis Testing Outline Logic for Hypothesis Testing Critical Value Alpha (α) level.05 level.01 OneTail versus TwoTail Tests critical values for both alpha levels Logic for Hypothesis
More informationCorrelational Research
Correlational Research Chapter Fifteen Correlational Research Chapter Fifteen Bring folder of readings The Nature of Correlational Research Correlational Research is also known as Associational Research.
More informationDescriptive Statistics
Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize
More informationDDBA 8438: Introduction to Hypothesis Testing Video Podcast Transcript
DDBA 8438: Introduction to Hypothesis Testing Video Podcast Transcript JENNIFER ANN MORROW: Welcome to "Introduction to Hypothesis Testing." My name is Dr. Jennifer Ann Morrow. In today's demonstration,
More informationIntroduction to Hypothesis Testing
I. Terms, Concepts. Introduction to Hypothesis Testing A. In general, we do not know the true value of population parameters  they must be estimated. However, we do have hypotheses about what the true
More informationIntroduction to. Hypothesis Testing CHAPTER LEARNING OBJECTIVES. 1 Identify the four steps of hypothesis testing.
Introduction to Hypothesis Testing CHAPTER 8 LEARNING OBJECTIVES After reading this chapter, you should be able to: 1 Identify the four steps of hypothesis testing. 2 Define null hypothesis, alternative
More informationAn Introduction to Statistics Course (ECOE 1302) Spring Semester 2011 Chapter 10 TWOSAMPLE TESTS
The Islamic University of Gaza Faculty of Commerce Department of Economics and Political Sciences An Introduction to Statistics Course (ECOE 130) Spring Semester 011 Chapter 10 TWOSAMPLE TESTS Practice
More informationPsychology 60 Fall 2013 Practice Exam Actual Exam: Next Monday. Good luck!
Psychology 60 Fall 2013 Practice Exam Actual Exam: Next Monday. Good luck! Name: 1. The basic idea behind hypothesis testing: A. is important only if you want to compare two populations. B. depends on
More informationHypothesis testing  Steps
Hypothesis testing  Steps Steps to do a twotailed test of the hypothesis that β 1 0: 1. Set up the hypotheses: H 0 : β 1 = 0 H a : β 1 0. 2. Compute the test statistic: t = b 1 0 Std. error of b 1 =
More informationHypothesis Testing  One Mean
Hypothesis Testing  One Mean A hypothesis is simply a statement that something is true. Typically, there are two hypotheses in a hypothesis test: the null, and the alternative. Null Hypothesis The hypothesis
More informationPearson's Correlation Tests
Chapter 800 Pearson's Correlation Tests Introduction The correlation coefficient, ρ (rho), is a popular statistic for describing the strength of the relationship between two variables. The correlation
More informationTwo Related Samples t Test
Two Related Samples t Test In this example 1 students saw five pictures of attractive people and five pictures of unattractive people. For each picture, the students rated the friendliness of the person
More informationIntroduction to Hypothesis Testing OPRE 6301
Introduction to Hypothesis Testing OPRE 6301 Motivation... The purpose of hypothesis testing is to determine whether there is enough statistical evidence in favor of a certain belief, or hypothesis, about
More informationWISE Power Tutorial All Exercises
ame Date Class WISE Power Tutorial All Exercises Power: The B.E.A.. Mnemonic Four interrelated features of power can be summarized using BEA B Beta Error (Power = 1 Beta Error): Beta error (or Type II
More informationStudy Guide for the Final Exam
Study Guide for the Final Exam When studying, remember that the computational portion of the exam will only involve new material (covered after the second midterm), that material from Exam 1 will make
More informationresearch/scientific includes the following: statistical hypotheses: you have a null and alternative you accept one and reject the other
1 Hypothesis Testing Richard S. Balkin, Ph.D., LPCS, NCC 2 Overview When we have questions about the effect of a treatment or intervention or wish to compare groups, we use hypothesis testing Parametric
More information1 Hypothesis Testing. H 0 : population parameter = hypothesized value:
1 Hypothesis Testing In Statistics, a hypothesis proposes a model for the world. Then we look at the data. If the data are consistent with that model, we have no reason to disbelieve the hypothesis. Data
More informationHYPOTHESIS TESTING (ONE SAMPLE)  CHAPTER 7 1. used confidence intervals to answer questions such as...
HYPOTHESIS TESTING (ONE SAMPLE)  CHAPTER 7 1 PREVIOUSLY used confidence intervals to answer questions such as... You know that 0.25% of women have red/green color blindness. You conduct a study of men
More informationC. The null hypothesis is not rejected when the alternative hypothesis is true. A. population parameters.
Sample Multiple Choice Questions for the material since Midterm 2. Sample questions from Midterms and 2 are also representative of questions that may appear on the final exam.. A randomly selected sample
More informationStatistics 2014 Scoring Guidelines
AP Statistics 2014 Scoring Guidelines College Board, Advanced Placement Program, AP, AP Central, and the acorn logo are registered trademarks of the College Board. AP Central is the official online home
More informationUNDERSTANDING THE DEPENDENTSAMPLES t TEST
UNDERSTANDING THE DEPENDENTSAMPLES t TEST A dependentsamples t test (a.k.a. matched or pairedsamples, matchedpairs, samples, or subjects, simple repeatedmeasures or withingroups, or correlated groups)
More informationHypothesis Testing. Hypothesis Testing
Hypothesis Testing Daniel A. Menascé Department of Computer Science George Mason University 1 Hypothesis Testing Purpose: make inferences about a population parameter by analyzing differences between observed
More informationII. DISTRIBUTIONS distribution normal distribution. standard scores
Appendix D Basic Measurement And Statistics The following information was developed by Steven Rothke, PhD, Department of Psychology, Rehabilitation Institute of Chicago (RIC) and expanded by Mary F. Schmidt,
More informationHypothesis Testing: Two Means, Paired Data, Two Proportions
Chapter 10 Hypothesis Testing: Two Means, Paired Data, Two Proportions 10.1 Hypothesis Testing: Two Population Means and Two Population Proportions 1 10.1.1 Student Learning Objectives By the end of this
More informationHypothesis testing. c 2014, Jeffrey S. Simonoff 1
Hypothesis testing So far, we ve talked about inference from the point of estimation. We ve tried to answer questions like What is a good estimate for a typical value? or How much variability is there
More informationLecture Notes Module 1
Lecture Notes Module 1 Study Populations A study population is a clearly defined collection of people, animals, plants, or objects. In psychological research, a study population usually consists of a specific
More informationHYPOTHESIS TESTING (ONE SAMPLE)  CHAPTER 7 1. used confidence intervals to answer questions such as...
HYPOTHESIS TESTING (ONE SAMPLE)  CHAPTER 7 1 PREVIOUSLY used confidence intervals to answer questions such as... You know that 0.25% of women have red/green color blindness. You conduct a study of men
More informationUNDERSTANDING THE INDEPENDENTSAMPLES t TEST
UNDERSTANDING The independentsamples t test evaluates the difference between the means of two independent or unrelated groups. That is, we evaluate whether the means for two independent groups are significantly
More information22. HYPOTHESIS TESTING
22. HYPOTHESIS TESTING Often, we need to make decisions based on incomplete information. Do the data support some belief ( hypothesis ) about the value of a population parameter? Is OJ Simpson guilty?
More informationExperimental Design. Power and Sample Size Determination. Proportions. Proportions. Confidence Interval for p. The Binomial Test
Experimental Design Power and Sample Size Determination Bret Hanlon and Bret Larget Department of Statistics University of Wisconsin Madison November 3 8, 2011 To this point in the semester, we have largely
More information6.2 Normal distribution. Standard Normal Distribution:
6.2 Normal distribution Slide Heights of Adult Men and Women Slide 2 Area= Mean = µ Standard Deviation = σ Donation: X ~ N(µ,σ 2 ) Standard Normal Distribution: Slide 3 Slide 4 a normal probability distribution
More informationPoint Biserial Correlation Tests
Chapter 807 Point Biserial Correlation Tests Introduction The point biserial correlation coefficient (ρ in this chapter) is the productmoment correlation calculated between a continuous random variable
More informationNonInferiority Tests for Two Means using Differences
Chapter 450 oninferiority Tests for Two Means using Differences Introduction This procedure computes power and sample size for noninferiority tests in twosample designs in which the outcome is a continuous
More informationRandomized Block Analysis of Variance
Chapter 565 Randomized Block Analysis of Variance Introduction This module analyzes a randomized block analysis of variance with up to two treatment factors and their interaction. It provides tables of
More informationBA 275 Review Problems  Week 5 (10/23/0610/27/06) CD Lessons: 48, 49, 50, 51, 52 Textbook: pp. 380394
BA 275 Review Problems  Week 5 (10/23/0610/27/06) CD Lessons: 48, 49, 50, 51, 52 Textbook: pp. 380394 1. Does vigorous exercise affect concentration? In general, the time needed for people to complete
More informationName: (b) Find the minimum sample size you should use in order for your estimate to be within 0.03 of p when the confidence level is 95%.
Chapter 78 Exam Name: Answer the questions in the spaces provided. If you run out of room, show your work on a separate paper clearly numbered and attached to this exam. Please indicate which program
More informationStatistiek I. Proportions aka Sign Tests. John Nerbonne. CLCG, Rijksuniversiteit Groningen. http://www.let.rug.nl/nerbonne/teach/statistieki/
Statistiek I Proportions aka Sign Tests John Nerbonne CLCG, Rijksuniversiteit Groningen http://www.let.rug.nl/nerbonne/teach/statistieki/ John Nerbonne 1/34 Proportions aka Sign Test The relative frequency
More informationOutline. Definitions Descriptive vs. Inferential Statistics The ttest  Onesample ttest
The ttest Outline Definitions Descriptive vs. Inferential Statistics The ttest  Onesample ttest  Dependent (related) groups ttest  Independent (unrelated) groups ttest Comparing means Correlation
More informationBA 275 Review Problems  Week 6 (10/30/0611/3/06) CD Lessons: 53, 54, 55, 56 Textbook: pp. 394398, 404408, 410420
BA 275 Review Problems  Week 6 (10/30/0611/3/06) CD Lessons: 53, 54, 55, 56 Textbook: pp. 394398, 404408, 410420 1. Which of the following will increase the value of the power in a statistical test
More informationSample Size and Power in Clinical Trials
Sample Size and Power in Clinical Trials Version 1.0 May 011 1. Power of a Test. Factors affecting Power 3. Required Sample Size RELATED ISSUES 1. Effect Size. Test Statistics 3. Variation 4. Significance
More informationEstimation of σ 2, the variance of ɛ
Estimation of σ 2, the variance of ɛ The variance of the errors σ 2 indicates how much observations deviate from the fitted surface. If σ 2 is small, parameters β 0, β 1,..., β k will be reliably estimated
More informationNovember 08, 2010. 155S8.6_3 Testing a Claim About a Standard Deviation or Variance
Chapter 8 Hypothesis Testing 8 1 Review and Preview 8 2 Basics of Hypothesis Testing 8 3 Testing a Claim about a Proportion 8 4 Testing a Claim About a Mean: σ Known 8 5 Testing a Claim About a Mean: σ
More informationHypothesis Testing for Beginners
Hypothesis Testing for Beginners Michele Piffer LSE August, 2011 Michele Piffer (LSE) Hypothesis Testing for Beginners August, 2011 1 / 53 One year ago a friend asked me to put down some easytoread notes
More informationPermutation Tests for Comparing Two Populations
Permutation Tests for Comparing Two Populations Ferry Butar Butar, Ph.D. JaeWan Park Abstract Permutation tests for comparing two populations could be widely used in practice because of flexibility of
More informationHYPOTHESIS TESTING WITH SPSS:
HYPOTHESIS TESTING WITH SPSS: A NONSTATISTICIAN S GUIDE & TUTORIAL by Dr. Jim Mirabella SPSS 14.0 screenshots reprinted with permission from SPSS Inc. Published June 2006 Copyright Dr. Jim Mirabella CHAPTER
More informationIndependent samples ttest. Dr. Tom Pierce Radford University
Independent samples ttest Dr. Tom Pierce Radford University The logic behind drawing causal conclusions from experiments The sampling distribution of the difference between means The standard error of
More informationComparing Two Groups. Standard Error of ȳ 1 ȳ 2. Setting. Two Independent Samples
Comparing Two Groups Chapter 7 describes two ways to compare two populations on the basis of independent samples: a confidence interval for the difference in population means and a hypothesis test. The
More information6: Introduction to Hypothesis Testing
6: Introduction to Hypothesis Testing Significance testing is used to help make a judgment about a claim by addressing the question, Can the observed difference be attributed to chance? We break up significance
More information12.5: CHISQUARE GOODNESS OF FIT TESTS
125: ChiSquare Goodness of Fit Tests CD121 125: CHISQUARE GOODNESS OF FIT TESTS In this section, the χ 2 distribution is used for testing the goodness of fit of a set of data to a specific probability
More informationWeek 3&4: Z tables and the Sampling Distribution of X
Week 3&4: Z tables and the Sampling Distribution of X 2 / 36 The Standard Normal Distribution, or Z Distribution, is the distribution of a random variable, Z N(0, 1 2 ). The distribution of any other normal
More informationAnalysis of Data. Organizing Data Files in SPSS. Descriptive Statistics
Analysis of Data Claudia J. Stanny PSY 67 Research Design Organizing Data Files in SPSS All data for one subject entered on the same line Identification data Betweensubjects manipulations: variable to
More informationSimple Regression Theory II 2010 Samuel L. Baker
SIMPLE REGRESSION THEORY II 1 Simple Regression Theory II 2010 Samuel L. Baker Assessing how good the regression equation is likely to be Assignment 1A gets into drawing inferences about how close the
More informationChapter 7 Notes  Inference for Single Samples. You know already for a large sample, you can invoke the CLT so:
Chapter 7 Notes  Inference for Single Samples You know already for a large sample, you can invoke the CLT so: X N(µ, ). Also for a large sample, you can replace an unknown σ by s. You know how to do a
More informationUsing Excel for inferential statistics
FACT SHEET Using Excel for inferential statistics Introduction When you collect data, you expect a certain amount of variation, just caused by chance. A wide variety of statistical tests can be applied
More informationConfidence intervals
Confidence intervals Today, we re going to start talking about confidence intervals. We use confidence intervals as a tool in inferential statistics. What this means is that given some sample statistics,
More informationBusiness Statistics, 9e (Groebner/Shannon/Fry) Chapter 9 Introduction to Hypothesis Testing
Business Statistics, 9e (Groebner/Shannon/Fry) Chapter 9 Introduction to Hypothesis Testing 1) Hypothesis testing and confidence interval estimation are essentially two totally different statistical procedures
More information"Statistical methods are objective methods by which group trends are abstracted from observations on many separate individuals." 1
BASIC STATISTICAL THEORY / 3 CHAPTER ONE BASIC STATISTICAL THEORY "Statistical methods are objective methods by which group trends are abstracted from observations on many separate individuals." 1 Medicine
More informationDifference of Means and ANOVA Problems
Difference of Means and Problems Dr. Tom Ilvento FREC 408 Accounting Firm Study An accounting firm specializes in auditing the financial records of large firm It is interested in evaluating its fee structure,particularly
More information1 Nonparametric Statistics
1 Nonparametric Statistics When finding confidence intervals or conducting tests so far, we always described the population with a model, which includes a set of parameters. Then we could make decisions
More informationTests for One Proportion
Chapter 100 Tests for One Proportion Introduction The OneSample Proportion Test is used to assess whether a population proportion (P1) is significantly different from a hypothesized value (P0). This is
More informationConsider a study in which. How many subjects? The importance of sample size calculations. An insignificant effect: two possibilities.
Consider a study in which How many subjects? The importance of sample size calculations Office of Research Protections Brown Bag Series KB Boomer, Ph.D. Director, boomer@stat.psu.edu A researcher conducts
More informationAdditional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jintselink/tselink.htm
Mgt 540 Research Methods Data Analysis 1 Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jintselink/tselink.htm http://web.utk.edu/~dap/random/order/start.htm
More informationLesson 1: Comparison of Population Means Part c: Comparison of Two Means
Lesson : Comparison of Population Means Part c: Comparison of Two Means Welcome to lesson c. This third lesson of lesson will discuss hypothesis testing for two independent means. Steps in Hypothesis
More informationThere are three kinds of people in the world those who are good at math and those who are not. PSY 511: Advanced Statistics for Psychological and Behavioral Research 1 Positive Views The record of a month
More informationCalculating, Interpreting, and Reporting Estimates of Effect Size (Magnitude of an Effect or the Strength of a Relationship)
1 Calculating, Interpreting, and Reporting Estimates of Effect Size (Magnitude of an Effect or the Strength of a Relationship) I. Authors should report effect sizes in the manuscript and tables when reporting
More informationCalculating PValues. Parkland College. Isela Guerra Parkland College. Recommended Citation
Parkland College A with Honors Projects Honors Program 2014 Calculating PValues Isela Guerra Parkland College Recommended Citation Guerra, Isela, "Calculating PValues" (2014). A with Honors Projects.
More informationindividualdifferences
1 Simple ANalysis Of Variance (ANOVA) Oftentimes we have more than two groups that we want to compare. The purpose of ANOVA is to allow us to compare group means from several independent samples. In general,
More informationStatistics Review PSY379
Statistics Review PSY379 Basic concepts Measurement scales Populations vs. samples Continuous vs. discrete variable Independent vs. dependent variable Descriptive vs. inferential stats Common analyses
More informationNONPARAMETRIC STATISTICS 1. depend on assumptions about the underlying distribution of the data (or on the Central Limit Theorem)
NONPARAMETRIC STATISTICS 1 PREVIOUSLY parametric statistics in estimation and hypothesis testing... construction of confidence intervals computing of pvalues classical significance testing depend on assumptions
More informationSCHOOL OF HEALTH AND HUMAN SCIENCES DON T FORGET TO RECODE YOUR MISSING VALUES
SCHOOL OF HEALTH AND HUMAN SCIENCES Using SPSS Topics addressed today: 1. Differences between groups 2. Graphing Use the s4data.sav file for the first part of this session. DON T FORGET TO RECODE YOUR
More informationIndependent t Test (Comparing Two Means)
Independent t Test (Comparing Two Means) The objectives of this lesson are to learn: the definition/purpose of independent ttest when to use the independent ttest the use of SPSS to complete an independent
More informationStatistics I for QBIC. Contents and Objectives. Chapters 1 7. Revised: August 2013
Statistics I for QBIC Text Book: Biostatistics, 10 th edition, by Daniel & Cross Contents and Objectives Chapters 1 7 Revised: August 2013 Chapter 1: Nature of Statistics (sections 1.11.6) Objectives
More informationSection 13, Part 1 ANOVA. Analysis Of Variance
Section 13, Part 1 ANOVA Analysis Of Variance Course Overview So far in this course we ve covered: Descriptive statistics Summary statistics Tables and Graphs Probability Probability Rules Probability
More informationTwo Correlated Proportions (McNemar Test)
Chapter 50 Two Correlated Proportions (Mcemar Test) Introduction This procedure computes confidence intervals and hypothesis tests for the comparison of the marginal frequencies of two factors (each with
More informationModule 2 Probability and Statistics
Module 2 Probability and Statistics BASIC CONCEPTS Multiple Choice Identify the choice that best completes the statement or answers the question. 1. The standard deviation of a standard normal distribution
More informationMULTIPLE REGRESSION AND ISSUES IN REGRESSION ANALYSIS
MULTIPLE REGRESSION AND ISSUES IN REGRESSION ANALYSIS MSR = Mean Regression Sum of Squares MSE = Mean Squared Error RSS = Regression Sum of Squares SSE = Sum of Squared Errors/Residuals α = Level of Significance
More informationAP STATISTICS (WarmUp Exercises)
AP STATISTICS (WarmUp Exercises) 1. Describe the distribution of ages in a city: 2. Graph a box plot on your calculator for the following test scores: {90, 80, 96, 54, 80, 95, 100, 75, 87, 62, 65, 85,
More informationTwosample hypothesis testing, II 9.07 3/16/2004
Twosample hypothesis testing, II 9.07 3/16/004 Small sample tests for the difference between two independent means For twosample tests of the difference in mean, things get a little confusing, here,
More informationChapter 2. Hypothesis testing in one population
Chapter 2. Hypothesis testing in one population Contents Introduction, the null and alternative hypotheses Hypothesis testing process Type I and Type II errors, power Test statistic, level of significance
More informationUnderstand the role that hypothesis testing plays in an improvement project. Know how to perform a two sample hypothesis test.
HYPOTHESIS TESTING Learning Objectives Understand the role that hypothesis testing plays in an improvement project. Know how to perform a two sample hypothesis test. Know how to perform a hypothesis test
More information3.4 Statistical inference for 2 populations based on two samples
3.4 Statistical inference for 2 populations based on two samples Tests for a difference between two population means The first sample will be denoted as X 1, X 2,..., X m. The second sample will be denoted
More informationTesting Hypotheses About Proportions
Chapter 11 Testing Hypotheses About Proportions Hypothesis testing method: uses data from a sample to judge whether or not a statement about a population may be true. Steps in Any Hypothesis Test 1. Determine
More informationSummary of Formulas and Concepts. Descriptive Statistics (Ch. 14)
Summary of Formulas and Concepts Descriptive Statistics (Ch. 14) Definitions Population: The complete set of numerical information on a particular quantity in which an investigator is interested. We assume
More informationMind on Statistics. Chapter 12
Mind on Statistics Chapter 12 Sections 12.1 Questions 1 to 6: For each statement, determine if the statement is a typical null hypothesis (H 0 ) or alternative hypothesis (H a ). 1. There is no difference
More informationNonInferiority Tests for One Mean
Chapter 45 NonInferiority ests for One Mean Introduction his module computes power and sample size for noninferiority tests in onesample designs in which the outcome is distributed as a normal random
More informationAP STATISTICS 2010 SCORING GUIDELINES
2010 SCORING GUIDELINES Question 4 Intent of Question The primary goals of this question were to (1) assess students ability to calculate an expected value and a standard deviation; (2) recognize the applicability
More information1. What is the critical value for this 95% confidence interval? CV = z.025 = invnorm(0.025) = 1.96
1 Final Review 2 Review 2.1 CI 1propZint Scenario 1 A TV manufacturer claims in its warranty brochure that in the past not more than 10 percent of its TV sets needed any repair during the first two years
More informationTests for Two Proportions
Chapter 200 Tests for Two Proportions Introduction This module computes power and sample size for hypothesis tests of the difference, ratio, or odds ratio of two independent proportions. The test statistics
More informationTwosample inference: Continuous data
Twosample inference: Continuous data Patrick Breheny April 5 Patrick Breheny STA 580: Biostatistics I 1/32 Introduction Our next two lectures will deal with twosample inference for continuous data As
More informationIntroduction to Analysis of Variance (ANOVA) Limitations of the ttest
Introduction to Analysis of Variance (ANOVA) The Structural Model, The Summary Table, and the One Way ANOVA Limitations of the ttest Although the ttest is commonly used, it has limitations Can only
More informationThe Null Hypothesis. Geoffrey R. Loftus University of Washington
The Null Hypothesis Geoffrey R. Loftus University of Washington Send correspondence to: Geoffrey R. Loftus Department of Psychology, Box 351525 University of Washington Seattle, WA 981951525 gloftus@u.washington.edu
More informationPermutation & NonParametric Tests
Permutation & NonParametric Tests Statistical tests Gather data to assess some hypothesis (e.g., does this treatment have an effect on this outcome?) Form a test statistic for which large values indicate
More informationHow To Test For Significance On A Data Set
NonParametric Univariate Tests: 1 Sample Sign Test 1 1 SAMPLE SIGN TEST A nonparametric equivalent of the 1 SAMPLE TTEST. ASSUMPTIONS: Data is nonnormally distributed, even after log transforming.
More informationIn the past, the increase in the price of gasoline could be attributed to major national or global
Chapter 7 Testing Hypotheses Chapter Learning Objectives Understanding the assumptions of statistical hypothesis testing Defining and applying the components in hypothesis testing: the research and null
More information