False Research and Scientism
Why should you trust the research coming from scientism?
Especially when their own people do research that uncovers the fact that most of their published findings are wrong:
“Why Most Published Research Findings Are False” is a 2005 essay written by John Ioannidis, a professor at the Stanford School of Medicine, and published in PLOS Medicine.[1] It is considered foundational to the field of metascience. In the paper, Ioannidis argued that a large number, if not the majority, of published medical research papers contain results that cannot be replicated. In simple terms, the essay states that scientists use hypothesis testing to determine whether scientific discoveries are significant. “Significance” is formalized in terms of probability, and one formalized calculation (“P value”) is reported in the scientific literature as a screening mechanism. Ioannidis posited assumptions about the way people perform and report these tests; then he constructed a statistical model which indicates that most published findings are false positive results.
[…]
Despite skepticism about extreme statements made in the paper, Ioannidis’s broader argument and warnings have been accepted by a large number of researchers. The growth of metascience and the recognition of a scientific replication crisis have bolstered the paper’s credibility, and led to calls for methodological reforms in scientific research.
The paper itself can be found here.
With a notable small excerpt:
There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.
Published research findings are sometimes refuted by subsequent evidence, with ensuing confusion and disappointment. Refutation and controversy is seen across the range of research designs, from clinical trials and traditional epidemiological studies [1–3] to the most modern molecular research [4,5]. There is increasing concern that in modern research, false findings may be the majority or even the vast majority of published research claims [6–8]. However, this should not be surprising. It can be proven that most claimed research findings are false. Here I will examine the key factors that influence this problem and some corollaries thereof.
The study itself is somewhat dense, but give it a full read when you can. It is incredibly insightful and really helps solidify the point about not trusting the claims from scientism, especially their claims that go against common sense.
If this one study doesn’t sell it for you (which it would, if you read it)—We also have tons of examples of the Ivory Tower literally making up data out of nothing. Or there are dozens of other studies since 2005 that have confirmed the findings of Ioannidis.
One other noteworthy example that grabs from many sources can be found here:
Time to assume that health research is fraudulent until proven otherwise?
Health research is based on trust. Health professionals and journal editors reading the results of a clinical trial assume that the trial happened and that the results were honestly reported. But about 20% of the time, said Ben Mol, professor of obstetrics and gynaecology at Monash Health, they would be wrong.
[…]
Mol, like Roberts, has conducted systematic reviews only to realise that most of the trials included either were zombie trials that were fatally flawed or were untrustworthy. What, he asked, is the scale of the problem? Although retractions are increasing, only about 0.04% of biomedical studies have been retracted, suggesting the problem is small. But the anaesthetist John Carlisle analysed 526 trials submitted to Anaesthesia and found that 73 (14%) had false data, and 43 (8%) he categorised as zombie. When he was able to examine individual patient data in 153 studies, 67 (44%) had untrustworthy data and 40 (26%) were zombie trials. Many of the trials came from the same countries (Egypt, China, India, Iran, Japan, South Korea, and Turkey), and when John Ioannidis, a professor at Stanford University, examined individual patient data from trials submitted from those countries to Anaesthesia during a year he found that many were false: 100% (7/7) in Egypt; 75% (3/ 4) in Iran; 54% (7/13) in India; 46% (22/48) in China; 40% (2/5) in Turkey; 25% (5/20) in South Korea; and 18% (2/11) in Japan. Most of the trials were zombies. Ioannidis concluded that there are hundreds of thousands of zombie trials published from those countries alone.
Others have found similar results, and Mol’s best guess is that about 20% of trials are false. Very few of these papers are retracted.
Be cautious of what you take from the Ivory Tower. If the study is not constantly attacked by everyone within scientism, it is probably bandwagon’d and fake. If it is constantly attacked by everyone in the tower (controversial), but still holds up for the most part, then it is probably true. Think The Bell Curve, for an example of the latter.
Always be skeptical, even of things that benefit the dissidents.
And finally, watch this short video for a comparison between genuine science and scientism.
Read Next:
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