Dichotomous thinking

In statistics, dichotomous thinking or binary thinking is the process of seeing a discontinuity in the possible values that a p-value can take during null hypothesis significance testing: it is either above the significance threshold (usually 0.05) or below. When applying dichotomous thinking, a first p-value of 0.0499 will be interpreted the same as a p-value of 0.0001 (the null hypothesis is rejected) while a second p-value of 0.0501 will be interpreted the same as a p-value of 0.7 (the null hypothesis is accepted). The fact that first and second p-values are mathematically very close is thus completely disregarded and values of p are not considered as continuous but are interpreted dichotomously with respect to the significance threshold. A common measure of dichotomous thinking is the cliff effect.[1]

Dichotomous thinking is very often associated with p-value reading[2][3][4] but it can also happen with other statistical tools such as interval estimates.[1][5]

See also

References
  1. Lai, Jerry (2019). "DICHOTOMOUS THINKING: A PROBLEM BEYOND NHST" (PDF). ICOTS8. Retrieved 23 October 2018.
  2. Rosenthal, Robert; Gaito, John (1963). "The Interpretation of Levels of Significance by Psychological Researchers". The Journal of Psychology. Informa UK Limited. 55 (1): 33–38. doi:10.1080/00223980.1963.9916596. ISSN 0022-3980.
  3. Nelson, Nanette; Rosenthal, Robert; Rosnow, Ralph L. (1986). "Interpretation of significance levels and effect sizes by psychological researchers". American Psychologist. American Psychological Association (APA). 41 (11): 1299–1301. doi:10.1037/0003-066x.41.11.1299. ISSN 1935-990X.
  4. Besançon, Lonni; Dragicevic, Pierre (2019). The Continued Prevalence of Dichotomous Inferences at CHI. New York, New York, USA: ACM Press. doi:10.1145/3290607.3310432. ISBN 978-1-4503-5971-9.
  5. Helske, Jouni; Helske, Satu; Cooper, Matthew; Ynnerman, Anders; Besançon, Lonni (2020-02-17). "Are You Sure You're Sure? -- Effects of Visual Representation on the Cliff Effect in Statistical Inference". arXiv:2002.07671 [stat.OT].
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