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Business Research Methods Chap017

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Business Research Methods Chap017

  1. 1. 17-1
  2. 2. Part Four ANALYSIS AND PRESENTATION OF DATA 17-2McGraw-Hill/Irwin © 2003 The McGraw-Hill Companies, Inc.,All Rights Reserved.
  3. 3. Chapter Seventeen HYPOTHESIS TESTING17-3
  4. 4. Approaches to Hypothesis Testing • Classical Statistics – sampling-theory approach – objective view of probability – decision making rests on analysis of available sampling data • Bayesian Statistics – extension of classical statistics – consider all other available information17-4
  5. 5. Types of Hypotheses • Null – that no statistically significant difference exists between the parameter and the statistic being compared • Alternative – logical opposite of the null hypothesis – that a statistically significant difference does exist between the parameter and the statistic being compared.17-5
  6. 6. Logic of Hypothesis Testing • Two tailed test – nondirectional test – considers two possibilities • One tailed test – directional test – places entire probability of an unlikely outcome to the tail specified by the alternative hypothesis17-6
  7. 7. Decision Errors in Testing • Type I error – a true null hypothesis is rejected • Type II error – one fails to reject a false null hypothesis17-7
  8. 8. Testing for Statistical Significance • State the null hypothesis • Choose the statistical test • Select the desired level of significance • Compute the calculated difference value • Obtain the critical value • Interpret the test17-8
  9. 9. Classes of Significance Tests • Parametric tests – Z or t test is used to determine the statistical significance between a sample distribution mean and a population parameter • Assumptions: – independent observations – normal distributions – populations have equal variances – at least interval data measurement scale17-9
  10. 10. Classes of Significance Tests Nonparametric tests – Chi-square test is used for situations in which a test for differences between samples is required • Assumptions – independent observations for some tests – normal distribution not necessary – homogeneity of variance not necessary – appropriate for nominal and ordinal data, may be used for interval or ratio data17-10
  11. 11. How to Test the Null Hypothesis • Analysis of variance (ANOVA) – the statistical method for testing the null hypothesis that means of several populations are equal17-11
  12. 12. Multiple Comparison Tests • Multiple comparison procedures – test the difference between each pair of means and indicate significantly different group means at a specified alpha level (<.05) – use group means and incorporate the MSerror term of the F ratio17-12
  13. 13. How to Select a Test • Which does the test involve? – one sample, – two samples – k samples • If two or k samples,are the individual cases independent or related? • Is the measurement scale nominal, ordinal, interval, or ratio?17-13
  14. 14. K Related Samples Test Use when: • The grouping factor has more than two levels • Observations or participants are – matched . . . or – the same participant is measured more than once • Interval or ratio data17-14

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