Nullius in Verba

In this episode, we discuss Quantifauxcation, described by statistician Philip Stark as “situations in which a number is, in effect, made up, and then is given credence merely because it is quantitative.” We give examples of quantifauxcation in psychology, including errors of the third kind. We spend the second half of the podcast discussing how to develop quantitative measures that are meaningful and bridge the divide between qualitative and quantitative observations.   Shownotes Statistics textbook by Philip Stark. Stark, P. B. (2022). Pay No attention to the model behind the curtain. Pure and Applied Geophysics, 179(11), 4121–4145. https://doi.org/10.1007/s00024-022-03137-2  Burgess, E. W. (1927). Statistics and case studies as methods of sociological research, Vol 12(3), 103-120. (Thanks to Andy Grieve!) Nick Brown's role in pointing out flaws in the positivity ratio. Retraction notice of the positivity ratio paper. Blog by Tania Lombrozo on nonsensical formulas in abstracts. Kimball, A. W. (1957). Errors of the third kind in statistical consulting. Journal of the American Statistical Association, 52(278), 133–142. https://doi.org/10.1080/01621459.1957.10501374 Type III errors: Philip Stark’s post of Deborah Mayo’s blog Brower, D. (1949). The problem of quantification in psychological science. Psychological Review, 56(6), 325–333. https://doi.org/10.1037/h0061802 Guttman scales Wilson, M. (2023). Constructing measures: An item response modeling approach. Taylor & Francis. Wilson, M., Bathia, S., Morell, L., Gochyyev, P., Koo, B. W., & Smith, R. (2022). Seeking a better balance between efficiency and interpretability: Comparing the likert response format with the Guttman response format. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000462 Bhatti, H.A., Mehta, S., McNeil, R., Wilson, M. (2023). A scientific approach to assessment: Rasch measurement and the four building blocks. In X. Liu & W. Boone (Eds.), Advances in Applications of Rash Measurement in Science Education. Springer Nature.   

In preparation for a discussion on Quantifauxcation, a reading of 'Problem-Centering vs. Means-Centering in Science' by Abraham H. Maslow (1946).  Maslow, A. H. (1946). Problem-Centering vs. Means-Centering in Science. Philosophy of Science, 13(4), 326–331. https://doi.org/10.1086/286907

In today’s episode, we discuss intellectual vices. How can we tell the difference between justified confidence and unjustified arrogance? How do we deal with feelings of envy or negative comparison with other scientists? What is the difference between building one’s career and careerism? And what do we do about scientists who do not care about the truth?    Shownotes Azrin, N. H., Holz, W., Ulrich, R., & Goldiamond, I. (1961). The control of the content of conversation through reinforcement. Journal of the Experimental Analysis of Behavior, 4, 25–30. https://doi.org/10.1901/jeab.1961.4-25  Meehl, P. E. (1967). Theory-testing in psychology and physics: A methodological paradox. Philosophy of Science, 103–115. https://doi.org/10.1086/288135   Mitroff, I. I. (1974). Norms and Counter-Norms in a Select Group of the Apollo Moon Scientists: A Case Study of the Ambivalence of Scientists. American Sociological Review, 39(4), 579–595. https://doi.org/10.2307/2094423 Susan Blackmore: https://en.wikipedia.org/wiki/Susan_Blackmore 

In this episode, we discuss scientific snobbery and the ways in which it affects our interactions with and perceptions of other scientists. What are the reasons for hierarchies among different disciplines, institutions, and approaches to science? What are some ways in which snobbery manifests in science? And is it snobby to not want to present scientific posters? Enjoy.    Shownotes:  Ego and Math (3Blue1Brown) M. V. Berry; Regular and irregular motion. AIP Conf. Proc. 15 September 1978; 46 (1): 16–120. https://doi.org/10.1063/1.31417  

Discussions on the replication crisis in social psychology, historical crises, concerns about negative consequences, publishing replications, the impact of statistical advances and computer technology on data manipulation, the importance of taking action and better science, relevance and real-world consequences

Reading of the chapter "Investigator Data Analysis Effect" from the book: Barber, T. X. (1976). Pitfalls in Human Research: Ten Pivotal Points. Pergamon Press.

This podcast explores the replication crisis in psychology, discussing key events such as Daryl Bem's precognition studies and the False Positive Psychology paper. The hosts share personal anecdotes and discuss the concept of crisis, controversial papers, data analysis methods, unethical research practices, and the Reproducibility Project's impact.

In this episode we reflect on the role of intelligence in scientist. How much does intelligence matter in science, and which other characteristics might play a role in doing good science? Do scientist need to be extremely intelligent or can anyone do science? And what is the role of stupidity in science?  Capax Mentis roughly translates to "capacity of mind." Smriti stupidly messed up her audio so the quality isn't great. Apologies!    Shownotes Schwartz, M. A. (2008). The importance of stupidity in scientific research. Journal of Cell Science, 121(11), 1771. https://doi.org/10.1242/jcs.033340 Bernal, J. D. (1939). The Social Function Of Science. Routledge. Paul Medawar: Advice to a Young Scientist  Feynman talking about the uncomfortable feeling of confusion   A good scientist always keeps learning – Nobel Laureate Peter Doherty  Flatland (1884) by Edwin Abbott Abbott  A zero-order correlation simply refers to the correlation between two variables (i.e., the independent and dependent variable) without controlling for the influence of any other variables. Essentially, this means that a zero-order correlation is the same thing as a Pearson correlation.

As prologue to the next episode on how smart one needs to be to be a scientist, we present a reading of chapter 2 "How can I tell if I am cut out to be a scientific research worker?" by Peter B. Medawar from his 1979 book 'Advice to a young scientist'. Our next episode was inspired by the section "Am I brainy enough to be a scientist?" https://www.google.nl/books/edition/Advice_To_A_Young_Scientist/3fg3DgAAQBAJ

In this episode we discuss Daniel Dennett's distinction between chess, or research worth doing, and 'chmess,'  research not worth doing. We discuss ways to determine whether our research is chess or chmess, and how to avoid being sucked into lines of research we don't particularly care about.    Shownotes Dennett, D. C. (2006). Higher-order truths about chmess. Topoi, 25, 39–41. Dunnette, M. D. (1966). Fads, fashions, and folderol in psychology. American Psychologist, 21(4), 343. Folderol means 'a useless ornament or accessory', and is used to indicate something is 'nonsense'.  Dweck, C. S. (2022). Mindsets: From bathtubs to hot beliefs to social change. In Kassin, S. (Ed.) Pillars of Social Psychology: Stories and Retrospectives, 213–219. Cambridge University Press.  The Kardashian Index