2004
Volume 41, Issue 1
  • ISSN: 1573-9775
  • E-ISSN: 2352-1236

Abstract

Abstract

Null-hypothesis significance testing is the major form of inferential statistics in the field of applied communication research. On many occasions, substantive conclusions are drawn solely on the basis of the (in)significance of a test result. The justification for these conclusions, however, is suspect, because the results of significance testing are too often misinterpreted. The questionable practice of drawing conclusions based on misunderstandings poses a significant problem for the scientific status of communication research. In this contribution the so-called big five misunderstandings (Kline, 2013) of significance testing are described and explained.

Loading

Article metrics loading...

/content/journals/10.5117/TVT2019.1.014.MULD
2019-04-01
2022-01-22
Loading full text...

Full text loading...

/deliver/fulltext/15739775/41/1/14_TVT2019.1_MULD.html?itemId=/content/journals/10.5117/TVT2019.1.014.MULD&mimeType=html&fmt=ahah

References

  1. Cohen, J.(1994). The earth is round (p < .05). American Psychologist, 49, 997-1003.
    [Google Scholar]
  2. Cumming, G. & Calin-Jageman, R.(2017). Introduction to the New Statistics. Estimation, Open Science, & Beyond. New York/Londen: Routledge.
    [Google Scholar]
  3. Gigerenzer, G., & Marewski, J.(2015). Surrogate science: The idol of a universal method for scientific inference. Journal of Management, 41, 421-440.
    [Google Scholar]
  4. Goodman, S.(2008). A dirty dozen: Twelve p-value misconceptions. Seminars in Hematology, 45, 135-140.
    [Google Scholar]
  5. Haller, H., & Krauss, S.(2002). Misinterpretations of Significance. A problem students share with their teachers?Methods of Psychological Research Online, 7(1). Zie https://www.metheval.uni-jena.de/lehre/0405-ws/evaluationuebung/haller.pdf
    [Google Scholar]
  6. Hubbard, R., & Bayarri, M.J.(2003). Confusion over measures of evidence (p’s) versus errors (α’s) in classical statistical testing (with comments). The American Statistician, 57, 171-182.
    [Google Scholar]
  7. Kline, R. B.(2013). Beyond Significance Testing. Statistics Reform in the Behavioral Sciences. Washington, DC: American Psychological Association.
  8. Kruschke, J.K.(2015). Doing Bayesian Data Analysis. A Tutorial with R, Jags, and Stan. (2nd ed.). London: Academic Press.
    [Google Scholar]
  9. Lambdin, C.(2012). Significance tests as sorcery: Science is empirical – significance tests are not. Theory & Psychology, 22, 67-90.
    [Google Scholar]
  10. Mulder. G.(2008). Understanding Causal Coherence Relations. Utrecht: LOT. Zie https://www.lotpublications.nl/Documents/172_fulltext.pdf
    [Google Scholar]
  11. Mulder, G.(2016). De kwaliteit van onderzoek. Dichotoom denken versus meta-analytisch denken. Tijdschrift voor Taalbeheersing, 38(2), 163-173.
    [Google Scholar]
  12. Oakes, M.(1986). Statistical Significance. New York, NY: Wiley.
    [Google Scholar]
  13. Zilliak, S., & McCloskey, D.N.(2008). The cult of statistical significance: How the standard error costs us jobs, justice, and lives. Ann Arbor: University of Michigan Press.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.5117/TVT2019.1.014.MULD
Loading
/content/journals/10.5117/TVT2019.1.014.MULD
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): methodology; p-values; significance testing
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error