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Tuesday, April 02, 2019

World’s top statisticians are urging scientists to ditch statistics and stick to mathematics | Statistics - ThePrint

Statistical significance can be misleading because it sets an arbitrary threshold on the level of uncertainty science should be willing to accept, as ThePrint reports.  

Photo: Representational image | Commons

Did you know that gorging on dark chocolate accelerates weight loss? A study published in 2015 found that a group of subjects who followed a low-carbohydrate diet and ate a bar of dark chocolate daily lost more weight than a group that followed the same diet sans chocolate. This discovery was heralded in some quarters as a scientific breakthrough.

If you’re still hesitant about raiding the supermarket chocolate aisle, rest assured: The study’s results are statistically significant. In theory, this means that the results would be improbable if chocolate did not contribute to weight loss, and therefore we can conclude that it does. A successful test of statistical significance has long been the admission ticket into the halls of scientific knowledge.

But not anymore, if statisticians have their way. In a coordinated assault last week, which included a special issue of the American Statistician and commentary in Nature (supported by 800 signatories), some of the discipline’s luminaries urged scientists to ditch the notion of statistical significance.

Critics argue that statistical significance can be misleading because it sets an arbitrary threshold on the level of uncertainty science should be willing to accept. Roughly speaking, uncertainty is expressed as the likelihood of observing an experimental result by chance, assuming the effect being tested doesn’t actually exist...

I agree that the term “statistical significance” is part of the problem; abandoning it is the right thing to do. In its place, statisticians advocate a more nuanced view of uncertainty. For example, scientists can report a range of possible conclusions that are compatible (to different degrees) with the data.

But the problem runs deeper. The broader issue is that the choice of a career in medicine, the life sciences or the social sciences (with some exceptions, like economics) isn’t typically indicative of a passion, or even an aptitude, for mathematics. Yet these sciences are thoroughly infused with statistics, and a shallow understanding of its principles gives rise to numerous fallacies.
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Source: ThePrint