Monthly Archives: June 2019

“The 2019 ASA Guide to P-values and Statistical Significance: Don’t Say What You Don’t Mean” (Some Recommendations)(ii)

Some have asked me why I haven’t blogged on the recent follow-up to the ASA Statement on P-Values and Statistical Significance (Wasserstein and Lazar 2016)–hereafter, ASA I. They’re referring to the editorial by Wasserstein, R., Schirm, A. and Lazar, N. (2019)–hereafter, ASA II–opening a special on-line issue of over 40 contributions responding to the call to describe “a world beyond P < 0.05”.[1] Am I falling down on the job? Not really. All of the issues are thoroughly visited in my Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars, SIST (2018, CUP). I invite interested readers to join me on the statistical cruise therein.[2] As the ASA II authors observe: “At times in this editorial and the papers you’ll hear deep dissonance, the echoes of ‘statistics wars’ still simmering today (Mayo 2018)”. True, and reluctance to reopen old wounds has only allowed them to fester. However, I will admit, that when new attempts at reforms are put forward, a philosopher of science who has written on the statistics wars ought to weigh in on the specific prescriptions/proscriptions, especially when a jumble of fuzzy conceptual issues are interwoven through a cacophony of competing reforms. (My published comment on ASA I, “Don’t Throw Out the Error Control Baby With the Bad Statistics Bathwater” is here.) Continue reading

Categories: ASA Guide to P-values, Statistics | 92 Comments

(Full) Excerpt. Excursion 5 Tour II: How Not to Corrupt Power (Power Taboos, Retro Power, and Shpower)

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returned from London…

The concept of a test’s power is still being corrupted in the myriad ways discussed in 5.5, 5.6.  I’m excerpting all of Tour II of Excursion 5, as I did with Tour I (of Statistical Inference as Severe Testing:How to Get Beyond the Statistics Wars 2018, CUP)*. Originally the two Tours comprised just one, but in finalizing corrections, I decided the two together was too long of a slog, and I split it up. Because it was done at the last minute, some of the terms in Tour II rely on their introductions in Tour I.  Here’s how it starts:

5.5 Power Taboos, Retrospective Power, and Shpower

Let’s visit some of the more populous tribes who take issue with power – by which we mean ordinary power – at least its post-data uses. Power Peninsula is often avoided due to various “keep out” warnings and prohibitions, or researchers come during planning, never to return. Why do some people consider it a waste of time, if not totally taboo, to compute power once we know the data? A degree of blame must go to N-P, who emphasized the planning role of power, and only occasionally mentioned its use in determining what gets “confirmed” post-data. After all, it’s good to plan how large a boat we need for a philosophical excursion to the Lands of Overlapping Statistical Tribes, but once we’ve made it, it doesn’t matter that the boat was rather small. Or so the critic of post-data power avers. A crucial disanalogy is that with statistics, we don’t know that we’ve “made it there,” when we arrive at a statistically significant result. The statistical significance alarm goes off, but you are not able to see the underlying discrepancy that generated the alarm you hear. The problem is to make the leap from the perceived alarm to an aspect of a process, deep below the visible ocean, responsible for its having been triggered. Then it is of considerable relevance to exploit information on the capability of your test procedure to result in alarms going off (perhaps with different decibels of loudness), due to varying values of the parameter of interest. There are also objections to power analysis with insignificant results. Continue reading

Categories: fallacy of non-significance, power, Statistical Inference as Severe Testing | Leave a comment

Don’t let the tail wag the dog by being overly influenced by flawed statistical inferences

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An article [i],“There is Still a Place for Significance Testing in Clinical Trials,” appearing recently in Clinical Trials, while very short, effectively responds to recent efforts to stop error statistical testing [ii]. We need more of this. Much more. The emphasis in this excerpt is mine: 

Much hand-wringing has been stimulated by the reflection that reports of clinical studies often misinterpret and misrepresent the findings of the statistical analyses. Recent proposals to address these concerns have included abandoning p-values and much of the traditional classical approach to statistical inference, or dropping the concept of statistical significance while still allowing some place for p-values. How should we in the clinical trials community respond to these concerns? Responses may vary from bemusement, pity for our colleagues working in the wilderness outside the relatively protected environment of clinical trials, to unease about the implications for those of us engaged in clinical trials…. Continue reading

Categories: statistical tests | Leave a comment

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