Hypothesis Testing & ANOVA

Erik Mooi, Marko Sarstedt and Irma Mooi-Reci
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Erik Mooi: University of Melbourne
Marko Sarstedt: Otto-von-Guericke-University
Irma Mooi-Reci: University of Melbourne

Chapter 6 in Market Research, 2018, pp 153-214 from Springer

Abstract: Abstract We first describe the essentials of hypothesis testing and how testing helps make critical business decisions of statistical and practical significance. Without using difficult mathematical formulas, we discuss the steps involved in hypothesis testing, the types of errors that may occur, and provide strategies on how to best deal with these errors. We also discuss common types of test statistics and explain how to determine which type you should use in which specific situation. We explain that the test selection depends on the testing situation, the nature of the samples, the choice of test, and the region of rejection. Drawing on a case study, we show how to link hypothesis testing logic to empirics in Stata. The case study touches upon different test situations and helps you interpret the tables and graphics in a quick and meaningful way.

Keywords: α-Inflation; α error; Analysis of Variance (ANOVA); β error; Bonferroni correction; Degrees of freedom; eta-squared; F-test; F-test of sample variance; Factor variable; Familywise error rate; Independent samples t-test; Interaction effect; Levene’s test; Mann-Whitney U test; Main effect; Nonparametric tests; Null and alternative hypothesis; Omega-squared; One-sample t-test; One-tailed test; One-way ANOVA; p-value; Paired samples t-test; Parametric test; Practical significance; Post hoc tests; Power of a test; Shapiro-Wilk test; Significance level; Sampling error; Statistical significance; t-test; Test statistic; Tukey’s honestly significant difference test; Two-sample t-test; Two-tailed test; Two-way ANOVA; Type I and type II error; Welch correction; Wilcoxon signed-rank test; z-test (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/978-981-10-5218-7_6

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