Hypothesis Testing and ANOVA

  • Marko Sarstedt
  • Erik Mooi
Part of the Springer Texts in Business and Economics book series (STBE)


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 SPSS. The case study touches upon different test situations and helps you interpret the tables and graphics in a quick and meaningful way.


Analysis Of Variance (ANOVA) Sale Display Between-group Mean Square (MSB) Mean Salary Within-group Mean Square (MSW) 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Agresti, A., & Finlay, B. (2014). Statistical methods for the social sciences (4th ed.). London: Pearson.Google Scholar
  2. Benjamin, D. J., et al. (2018). Redefine statistical significance. Nature Human Behaviour, 2, 6–10.Google Scholar
  3. Boneau, C. A. (1960). The effects of violations of assumptions underlying the t test. Psychological Bulletin, 57(1), 49–64.CrossRefGoogle Scholar
  4. Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159.CrossRefGoogle Scholar
  5. Everitt, B. S., & Skrondal, A. (2010). The Cambridge dictionary of statistics (4th ed.). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  6. Field, A. (2013). Discovering statistics using SPSS (4th ed.). London: Sage.Google Scholar
  7. Hubbard, R., & Bayarri, M. J. (2003). Confusion over measure of evidence (p’s) versus errors (α’s) in classical statistical testing. The American Statistician, 57(3), 171–178.CrossRefGoogle Scholar
  8. Kimmel, H. D. (1957). Three criteria for the use of one-tailed tests. Psychological Bulletin, 54(4), 351–353.CrossRefGoogle Scholar
  9. Lehmann, E. L. (1993). The Fischer, Neyman-Pearson theories of testing hypotheses: One theory or two? Journal of the American Statistical Association, 88(424), 1242–1249.CrossRefGoogle Scholar
  10. Lakens, D., et al. (2018). Justify your alpha. Nature Human Behaviour, 2, 168–171.Google Scholar
  11. Levene, H. (1960). Robust tests for equality of variances. In I. Olkin (Ed.) Contributions to probability and statistics (pp. 278–292). Palo Alto, CA: Stanford University Press.Google Scholar
  12. Liao, T. F. (2002). Statistical group comparison. New York, NJ: Wiley-InterScience.CrossRefGoogle Scholar
  13. Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. The Annals of Mathematical Statistics, 18(1), 50–60.CrossRefGoogle Scholar
  14. Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics. Advances in Health Sciences Education, 15(5), 625–632.CrossRefGoogle Scholar
  15. Nuzzo, R. (2014). Scientific method: Statistical errors. Nature, 506(7487), 150–152.CrossRefGoogle Scholar
  16. Ruxton, G. D., & Neuhaeuser, M. (2010). When should we use one-tailed hypothesis testing? Methods in Ecology and Evolution, 1(2), 114–117.CrossRefGoogle Scholar
  17. Schuyler, W. H. (2011). Readings statistics and research (6th ed). London: Pearson.Google Scholar
  18. Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3/4), 591–611.CrossRefGoogle Scholar
  19. Van Belle, G. (2008). Statistical rules of thumb (2nd ed.). Hoboken, N.J.: John Wiley & Sons.CrossRefGoogle Scholar
  20. Wasserstein, R. L., & Lazar, N. A. (2016). The ASA’s statement on p-values: Context, process, and purpose. The American Statistician, 70(2), 129–133.CrossRefGoogle Scholar
  21. Welch, B. L. (1951). On the comparison of several mean values: An alternative approach. Biometrika, 38(3/4), 330–336.CrossRefGoogle Scholar

Further Readings

  1. Kanji, G. K. (2006). 100 statistical tests (3rd ed.). London: Sage.CrossRefGoogle Scholar
  2. Van Belle, G. (2011). Statistical rules of thumb (2nd ed.). Hoboken, N.J.: John Wiley & Sons.Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Marko Sarstedt
    • 1
  • Erik Mooi
    • 2
  1. 1.Faculty of Economics and ManagementOtto-von-Guericke- University MagdeburgMagdeburgGermany
  2. 2.Department of Management and MarketingThe University of MelbourneParkville, VICAustralia

Personalised recommendations