Nature reports that three statisticians and more than 800 signatories call argue for scientists to abandon statistical significance:
How do statistics so often lead scientists to deny differences that those not educated in statistics can plainly see? For several generations, researchers have been warned that a statistically non-significant result does not ‘prove’ the null hypothesis (the hypothesis that there is no difference between groups or no effect of a treatment on some measured outcome)1. Nor do statistically significant results ‘prove’ some other hypothesis. Such misconceptions have famously warped the literature with overstated claims and, less famously, led to claims of conflicts between studies where none exists. …
We must learn to embrace uncertainty. One practical way to do so is to rename confidence intervals as ‘compatibility intervals’ and interpret them in a way that avoids overconfidence. Specifically, we recommend that authors describe the practical implications of all values inside the interval, especially the observed effect (or point estimate) and the limits. In doing so, they should remember that all the values between the interval’s limits are reasonably compatible with the data, given the statistical assumptions used to compute the interval7,10. Therefore, singling out one particular value (such as the null value) in the interval as ‘shown’ makes no sense. Valentin Amrhein, Sander Greenland & Blake McShane, “Scientists rise up against statistical significance” at Nature
Retraction Watch interviewed statistician Nicole Lazar, who explains:
One such principle about which there has been contentious debate, especially in the Frequentist versus Bayesian wars, is objectivity. It is important to understand and accept that while objectivity should be the goal of scientific research, pure objectivity can never be achieved. Science entails intrinsically subjective decisions, and expert judgment – applied with as much objectivity and as little bias as possible – is essential to sound science.
Hold that thought when another flatulent editorial in a local news source extols the “objectivity of science” while recommending some bad policy or other based on a probably questionable study.
Let’s see where this goes. Will it lead to less magic with numbers or more and bigger magic?
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