 Hypothesis TestingThomas Cleff ()
Chapter Chapter 9 in Applied Statistics and Multivariate Data Analysis for Business and Economics, 2025, pp 279-382 from Springer Abstract: Abstract This chapter provides a comprehensive exploration of hypothesis testing. It explores the principles of falsification as advocated by Karl Popper and the application of these principles in statistical hypothesis testing through the Neyman–Pearson approach. The chapter covers the construction and testing of null (H0) and alternative (H1) hypotheses, the interpretation of Type I and Type II errors (α and β errors). It provides practical examples using statistical software such as R, SPSS, Stata, and Excel, and explains various parametric and nonparametric tests including z-tests, t-tests, ANOVA, Mann–Whitney U test, Kruskal–Wallis test, Wilcoxon signed-rank test, Wilcoxon rank-sum test, chi-square test, as well as tests for normal distribution. Keywords: Hypothesis testing; Type I and Type II errors; α and β errors; z-tests; t-tests; Analysis of variance (ANOVA); Mann–Whitney U test; Kruskal–Wallis test; Wilcoxon signed-rank test; Wilcoxon rank-sum test; Chi-square test; Tests for normal distribution (search for similar items in EconPapers) There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:sptchp:978-3-031-78070-7_9 Ordering information: This item can be ordered from DOI: 10.1007/978-3-031-78070-7_9 Access Statistics for this chapter More chapters in Springer Texts in Business and Economics from Springer |
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