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Chapter 8

Chapter 8. Introducing Inferential Statistics. CHAPTER OBJECTIVES - STUDENTS SHOULD BE ABLE TO:. Explain the difference between descriptive and inferential statistics. Define the central limit theorem and explain why it is important to the world of inferential statistics.

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Chapter 8

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  1. Chapter 8 Introducing Inferential Statistics

  2. CHAPTER OBJECTIVES - STUDENTS SHOULD BE ABLE TO: • Explain the difference between descriptive and inferential statistics. • Define the central limit theorem and explain why it is important to the world of inferential statistics. • List the steps in completing a test of statistical significance. • Discuss the basic types of statistical tests and how they are used. • Explain Type I and Type II errors in null hypothesis testing. • Discuss the distinction between statistical significance and meaningful significance.

  3. CHAPTER OVERVIEW • Say Hello to Inferential Statistics • The Idea of Statistical Significance • Tests of Significance • Significance Versus Meaningfulness • Meta-analysis

  4. SAY HELLO TO INFERENTIAL STATISTICS • Descriptive statistics provide basic measures of a distribution of scores • Inferential statistics allow inferences to a larger population from the sample

  5. HOW INFERENCE WORKS • Representative samples from two groups are selected • Participants are tested • Means from each group are compared • Researchers conclude that measured differences between groups either • Result from chance, or • Reflect true differences • A conclusion is drawn regarding the role group membership plays in observed differences

  6. THE ROLE OF CHANCE • Chance is the first explanation for observed differences • Chance is unexplained variability • The goal of science is to • Control sources of variability, thus • Reducing the role of chance as an explanation

  7. THE CENTRAL LIMIT THEOREM • The means of samples drawn from a population will be normally distributed • This is so regardless of the shape of the population distribution • This demonstrates the power of inference

  8. AN EXAMPLE OF THE CENTRAL LIMIT THEOREM

  9. THE IDEA OF STATISTICAL SIGNIFICANCE • Because sampling is imperfect • Samples may not ideally match the population, and • Because hypotheses cannot be directly tested • Inference is subject to error

  10. STATISTICAL SIGNIFICANCE • The degree of risk that you are willing to take that you will reject a null hypothesis when it is actually true

  11. MAKING A DECISION

  12. TYPE I AND TYPE II ERRORS • The probability of making a Type II error • Not directly controlled by researcher • Reduced by increasing sample size • The probability of making a Type I error • Set by researcher • e.g., .01 = 1% chance of rejecting null when it is true • e.g., .05 = 5% chance of rejecting null when it is true • Not the probability of making one or more Type I errors on multiple tests of null!

  13. HOW A TEST OF SIGNIFICANCE WORKS • Each type of null hypothesis is tested with a particular statistic • Each statistic is characterized by a unique distribution of values that are used to evaluate the sample data

  14. USING A STATISTICAL TEST • State the null hypothesis Ho: µ1 = µ2 • Establish significance level e.g., p = .05 e.g., p = .01

  15. USING A STATISTICAL TEST • Select appropriate test statistic • Compute test statistic (obtained value) • Determine value needed to reject null (critical value), which depends on • Level of significance chosen (e.g., p = 0.5) • Degrees of freedom (based on sample size) • Compare obtained value to critical value • If obtained value > critical value, reject null • If obtained value  critical value, accept null

  16. t-TEST FOR INDEPENDENT MEANSUsed to test null hypothesis when two independent, unrelated groups are compared E.g., Chen and Stevenson (1989) • Ho: µ1980 =µ1984 • p = .05 • t-test • 2.00 • 1.980 • 2.00 > 1.980; p < .05 • State null • Establish level of risk • Select test statistic • Compute value • Determine critical value • Compare obtained value

  17. WHAT DOES t120 = 2.00, p < .05 Really Mean? • t = type of test • 120 = degrees of freedom • (related to sample size) • 2.00 = obtained value of t-test • p = probability • .05 = level of significance • (Type I error rate)

  18. A NEW TWIST TO p < .05 • A statement of probability, e.g., p < .05 indicates that the probability of making a Type I error on a test is less than .05 • But SPSS and other data analysis software compute exact probabilities, e.g., p = .0375

  19. LOOKING AT DIFFERENCES BETWEEN GROUPS

  20. t-TEST FOR DEPENDENT MEANS • Ho: µ1A = µ1B • p = .01 • t-test • 2.581 • 2.771 • 2.581 < 2.771; p > .01 • State null • Establish level of risk • Select test statistic • Compute value • Determine critical value • Compare obtained value

  21. LOOKING AT RELATIONSHIPS BETWEEN VARIABLES

  22. MULTIVARIATE ANALYSIS OF VARIANCE (MANOVA) • Simultaneously tests differences between groups on multiple dependent variables, but • Because dependent variables might be related • True Type I Error rate is inflated 1 – (1 - )k = Type I error rate k = number of pairwise comparisons • So, MANOVA takes these possible relationships into account

  23. FACTOR ANALYSIS • A factor groups several related measures into one construct • The new construct is treated as a dependent variable • This technique allows a researcher to more efficiently examine how these sets of variables are related

  24. SIGNIFICANCE VERSUS MEANINGFULNESS • Statistical significance refers to the • Probability that chance influenced observed differences • It does NOT refer to the meaningfulness or “importance” of observed differences • Statistical significance must be interpreted within a larger context

  25. META-ANALYSIS • Compares the results of multiple independent studies that have examined the same conceptual, dependent variable • Allows examination of trends and patterns that may exist in many different groups in many different studies

  26. HOW META-ANALYSES ARE DONE • An adequate sample of studies is collected • Results from these studies are converted to a common measure—usually effect size • Important aspects of the study are coded • Descriptive and correlational techniques are used to look for trends or common patterns in the outcomes of the group of studies

  27. HAVE WE MET OUR OBJECTIVES? CAN YOU: • Explain the difference between descriptive and inferential statistics? • Define the central limit theorem and explain why it is important to the world of inferential statistics? • List the steps in completing a test of statistical significance? • Discuss the basic types of statistical tests and how they are used? • Explain Type I and Type II errors in null hypothesis testing? • Discuss the distinction between statistical significance and meaningful significance?

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