{"id":988,"date":"2017-04-05T11:32:45","date_gmt":"2017-04-05T11:32:45","guid":{"rendered":"https:\/\/www.editage.com\/insights\/why-you-should-be-a-skeptical-scientist\/"},"modified":"2025-02-18T11:42:12","modified_gmt":"2025-02-18T11:42:12","slug":"why-you-should-be-a-skeptical-scientist","status":"publish","type":"post","link":"https:\/\/www.editage.com\/insights\/why-you-should-be-a-skeptical-scientist","title":{"rendered":"Why you should be a skeptical scientist"},"content":{"rendered":"

[This post has been reproduced with permission from Tim van der Zee. It was originally published on his <\/em>blog<\/em><\/a>.]<\/em><\/p>\n

Don\u2019t take my word for it, but being a scientist is about being a skeptic.<\/p>\n

About not being satisfied with easy answers to hard problems.<\/p>\n

About not believing something merely because it seems plausible\u2026<\/p>\n

.. nor about reading a scientific study and believing its conclusions because, again, it seems plausible.
\u00a0<\/p>\n

\n

“In some of my darker moments, I can persuade myself that all assertions in education:<\/p>\n

(a) derive from no evidence whatsoever (adult learning theory),<\/p>\n

(b) proceed despite contrary evidence (learning styles, self-assessment skills), or<\/p>\n

(c) go far beyond what evidence exists.”<\/p>\n

\u2013 Geoff Norman<\/p>\n<\/blockquote>\n

The scientific literature is biased. Positive results are published widely, while negative and null results gather dust in file drawers1, 2<\/sup>. This bias functions at many levels, from which papers are submitted to which papers are published3, 4<\/sup>. In turn, this incentivizes researchers to (consciously or unconsciously) engage in questionable research methods such as running many statistical tests but only reporting the \u2018successful\u2019 ones, called p-hacking 5<\/sup>. Furthermore, researchers often give a biased interpretation of one\u2019s own results, use causal language when this isn\u2019t warranted, and misleadingly cite others\u2019 results6<\/sup>. For example: close to 28% of citations are faulty or misleading, which typically goes undetected as most readers do not check the references7<\/sup>.<\/p>\n

This is certainly not all. Studies which have to adhere to a pre-registered protocol, such as clinical trials, often deviate from the protocol by not reporting outcomes or silently adding new outcomes8<\/sup>. Such changes are not random, but typically favor reporting positive effects and hiding negative ones9<\/sup>. This is not at all unique to clinical trials; published articles in general frequently include incorrectly reported statistics, with 35% including substantial errors which directly affect the conclusions10, 11, 12<\/sup>. Meta-analyses from authors with industry involvement are massively published yet fail to report caveats13<\/sup>. Besides, when the original studies are of low quality, a meta-analysis will not magically fix this (aka the \u2018garbage in, garbage out\u2019 principle). One such cause for low quality studies is the lack of control groups, or what can be even more misleading: inappropriate control groups which can incorrectly imply that placebo effects and other alternative explanations have been ruled out14<\/sup>. Note that these issues are certainly not restricted to quantitative research or (semi-)positivistic paradigms, but are just as relevant for qualitative research from a more naturalistic perspective15, 16, 17<\/sup>.<\/p>\n

Everybody lies<\/strong><\/p>\n

This list could go on for much longer, but the point has been made; everybody lies. In the current system, lying and misleading is not only very simple, it is incentivized. Partly this is due to the publication system, which strongly favors positive findings with a good story. In addition to incentives built into the publication system, individual researchers of course also play a fundamental role. However, what makes it especially tricky is that it is also partly inherent to many fields, especially those which do not have \u2018proof by technology\u2019. For example, if you claim you can make a better smartphone, you just build it. But in fields like psychology this is rarely possible. The variables are often latent, and not directly observable. The measurements are indirect, and it is often impossible to prove what they actually measure, if anything.<\/p>\n

Bad incentives won\u2019t disappear overnight. People tend to be resistant to change. While there are many who actively fight to improve science, it will be a long, if not never-ending journey until these efforts bear fruit.<\/p>\n

And now what\u2026<\/strong><\/p>\n

Is this an overly cynical observation? Maybe. Either way, it is paramount that we should be cautious. We should be skeptical of what we read. What is more, we should be very skeptical about what we do, about our own research.<\/p>\n

This is perhaps the prime reason why I started my blog: I am wrong most of the time. But I want to learn and be slightly less wrong over time. We need each other for that, because it is just too easy to fool oneself.<\/p>\n

Let\u2019s be skeptical scientists.<\/p>\n

Let\u2019s become better scientists.<\/p>\n

References\u00a0<\/strong><\/p>\n

    \n
  1. Dwan, K., Gamble, C., Williamson, P. R., & Kirkham, J. J. (2013).\u00a0Systematic review of the empirical evidence of study publication bias and outcome reporting bias\u2014an updated review<\/a>.\u00a0PloS one<\/em>,\u00a08<\/em>(7).<\/li>\n
  2. Franco, A., Malhotra, N., & Simonovits, G. (2014). Publication bias in the social sciences: Unlocking the file drawer<\/a>.\u00a0Science<\/em>,\u00a0345<\/em>(6203), 1502-1505.<\/li>\n
  3. Coursol, A., & Wagner, E. E. (1986).\u00a0Effect of positive findings on submission and acceptance rates: A note on meta-analysis bias<\/a>.\u00a0Professional Psychology: Research and Practice<\/em>, 17(2), 136-137<\/li>\n
  4. Kerr, S., Tolliver, J., & Petree, D. (1977).\u00a0Manuscript characteristics which influence acceptance for management and social science journals<\/a>.\u00a0Academy of Management Journal<\/em>,\u00a020<\/em>(1), 132-141.<\/li>\n
  5. Head, M. L., Holman, L., Lanfear, R., Kahn, A. T., & Jennions, M. D. (2015).\u00a0The extent and consequences of p-hacking in science<\/a>.\u00a0PLoS Biol<\/em>,\u00a013<\/em>(3).<\/li>\n
  6. Brown, A. W., Brown, M. M. B., & Allison, D. B. (2013).\u00a0Belief beyond the evidence: using the proposed effect of breakfast on obesity to show 2 practices that distort scientific evidence<\/a>.\u00a0The American journal of clinical nutrition<\/em>,\u00a098<\/em>(5), 1298-1308.<\/li>\n
  7. Van der Zee, T. & Nonsense, B. S. (2016).\u00a0It is easy to cite a random paper as support for anything<\/a>.\u00a0Journal of Misleading Citations<\/em>,\u00a033<\/em>(2), 483-475.<\/li>\n
  8. http:\/\/compare-trials.org\/<\/a><\/li>\n
  9. Jones, C. W., Keil, L. G., Holland, W. C., Caughey, M. C., & Platts-Mills, T. F. (2015).\u00a0Comparison of registered and published outcomes in randomized controlled trials: a systematic review<\/a>.\u00a0BMC medicine<\/em>,\u00a013<\/em>(1), 1.<\/li>\n
  10. Bakker, M., & Wicherts, J. M. (2011).\u00a0The (mis) reporting of statistical results in psychology journals<\/a>.\u00a0Behavior Research Methods<\/em>,\u00a043<\/em>(3), 666-678.<\/li>\n
  11. Nuijten, M. B., Hartgerink, C. H., van Assen, M. A., Epskamp, S., & Wicherts, J. M. (2015).\u00a0The prevalence of statistical reporting errors in psychology<\/a>\u00a0(1985\u20132013).\u00a0Behavior research methods<\/em>, 1-22.<\/li>\n
  12. Nonsense, B. S., &\u00a0Van der Zee, T. (2015).\u00a0The reported thirty-five percent is incorrect, it is approximately fifteen percent<\/a>.\u00a0The Journal of False Statistics<\/em>,\u00a033<\/em>(2), 417-424.<\/li>\n
  13. Ebrahim, S., Bance, S., Athale, A., Malachowski, C., & Ioannidis, J. P. (2015).\u00a0Meta-analyses with industry involvement are massively published and report no caveats for antidepressants<\/a>.\u00a0Journal of clinical epidemiology<\/em>.<\/li>\n
  14. Boot, W. R., Simons, D. J., Stothart, C., & Stutts, C. (2013).\u00a0The pervasive problem with placebos in psychology why active control groups are not sufficient to rule out placebo effects<\/a>.\u00a0Perspectives on Psychological Science<\/em>,\u00a08<\/em>(4), 445-454.<\/li>\n
  15. Collier, D., & Mahoney, J. (1996).\u00a0Insights and pitfalls: Selection bias in qualitative research<\/a>.\u00a0World Politics<\/em>,\u00a049<\/em>(01), 56-91.<\/li>\n
  16. Golafshani, N. (2003).\u00a0Understanding reliability and validity in qualitative research<\/a>.\u00a0The qualitative report<\/em>,\u00a08<\/em>(4), 597-606.<\/li>\n
  17. Sandelowski, M. (1986).\u00a0The problem of rigor in qualitative research<\/a>.\u00a0Advances in nursing science<\/em>,\u00a08<\/em>(3), 27-37.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    [This post has been reproduced with permission from Tim van der Zee. It was originally published on his blog.] Don\u2019t take my word for it, but being a scientist is about being a skeptic. About not being satisfied with easy answers to hard problems. About not believing something merely because it seems plausible\u2026 .. nor […]<\/p>\n","protected":false},"author":258,"featured_media":33313,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[2405],"tags":[99,39,366],"new_categories":[],"new_tags":[],"series":[],"class_list":["post-988","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry-trends","tag-ethical-guidelines","tag-publication-bias","tag-statistical-reporting"],"acf":[],"yoast_head":"\nWhy you should be a skeptical scientist | Editage Insights<\/title>\n<meta name=\"description\" content=\"Scientists should be skeptical of published science and what they publish in a world of reporting bias and questionable research methods\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" 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