Random and Systematic Errors in Context

  • Gideon J. MellenberghEmail author


The main objective of scientific research is to solve problems. Empirical studies are affected by random and systematic errors. Random errors decrease the precision of study results, but do not bias these results. In contrast, systematic errors bias study results. Errors and methods to prevent and correct errors are introduced, and are put into the context of the different parts of empirical studies (i.e., research questions, literature review, sampling, operationalizations, design, implementation, data analysis, and reporting).


Precision and bias of study results Prevention and correction of errors 


  1. Adèr, H. J. (2008). Phases and initial steps in data analysis. In H. J. Adèr & G. J. Mellenbergh (with contributions by D. J. Hand), Advising on research methods: A consultant’s companion (pp. 333–356). Huizen, The Netherlands: van Kessel.Google Scholar
  2. Cumming, G. (2012). Understanding the new statistics: Effect sizes, confidence intervals, and meta-analysis. London, England: Routledge.Google Scholar
  3. de Groot, A. D. (1969). Methodology: Foundations of inference and research in the behavioral sciences. The Hague, The Netherlands: Mouton.Google Scholar
  4. Harlow, L. L. (2017). The making of Psychological Methods. Psychological Methods, 22, 1–5.CrossRefGoogle Scholar
  5. Matzke, D., Nieuwenhuis, S., van Rijn, H., Slagter, H. A., van der Molen, M. W., & Wagenmakers, E.-J. (2015). The effect of horizontal eye movements on free recall: A preregistered adversarial collaboration. Journal of Experimental Psychology: General, 144, e1–e15.CrossRefGoogle Scholar
  6. Mellenbergh, G, J. (2008). General issues of research design. In H. J. Adèr & G. J. Mellenbergh (with contributions by D. J. Hand), Advising on research methods: A consultant’s companion (pp. 107–128). Huizen, The Netherlands: van Kessel.Google Scholar
  7. Munafò, M. R., Nosek, B. A., Bishop, D. V. M., Button, K. S., Chambers, C. D., du Sert, N. P., et al. (2017). A manifesto for reproducible science. Nature Human Behavior, 1, 1–9.Google Scholar
  8. Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. New York, NY: Houghton Mifflin.Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Emeritus Professor Psychological Methods, Department of PsychologyUniversity of AmsterdamAmsterdamThe Netherlands

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