(Excerpts from) ‘P-Value Thresholds: Forfeit at Your Peril’ (free access)

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A key recognition among those who write on the statistical crisis in science is that the pressure to publish attention-getting articles can incentivize researchers to produce eye-catching but inadequately scrutinized claims. We may see much the same sensationalism in broadcasting metastatistical research, especially if it takes the form of scapegoating or banning statistical significance. A lot of excitement was generated recently when Ron Wasserstein, Executive Director of the American Statistical Association (ASA), and co-editors A. Schirm and N. Lazar, updated the 2016 ASA Statement on P-Values and Statistical Significance (ASA I). In their 2019 interpretation, ASA I “stopped just short of recommending that declarations of ‘statistical significance’ be abandoned,” and in their new statement (ASA II) announced: “We take that step here….’statistically significant’ –don’t say it and don’t use it”. To herald the ASA II, and the special issue “Moving to a world beyond ‘p < 0.05’”, the journal Nature requisitioned a commentary from Amrhein, Greenland and McShane “Retire Statistical Significance” (AGM). With over 800 signatories, the commentary received the imposing title “Scientists rise up against significance tests”!

     Tom Hardwicke and John Ioannidis surveyed those signatories and give a report on the respondents (Hardwicke and Ioannidis 2019). I was invited to write an editorial on any aspect of the episode (“P-value thresholds: Forfeit at your peril“)–the opening of which is above. Hardwicke and Ioannidis 2019, a preprint of my editorial, and an editorial by Andrew Gelman are currently “free access” in the European Journal of Clinical Investigation. I guess that means these versions are currently freely accessible.
My article continues:

Note: By “ASA II” I allude only to the authors’ general recommendations, not their summaries of the 43 papers in the issue.)

Hardwicke and Ioannidis (2019) worry that recruiting signatories on such a paper politicizes the process of evaluating a stance on scientific method, and fallaciously appeals to popularity (argumentum ad populum) “because it conflates justification of a belief with the acceptance of a belief by a given group of people”. Opposing viewpoints are not given a similar forum. Fortunately, John Ioannidis (2019) can come out with a note in JAMA challenging ASA II and AGM, but the vast majority of stakeholders in the debate go unheard. Appealing to popularity gives a prudential reason to go along, it is risky to stand in opposition to the hundreds who signed, not to mention, the thought leaders at the ASA. There is also an appeal to fear, with the result that many will fear using statistical significance tests altogether. Why risk using a method that is persecuted with such zeal and fanfare?

Ioannidis (2019) points out what may not be obvious at first: it is not just a word ban but a gatekeeper ban:

Many fields of investigation … have major gaps in the ways they conduct, analyze, and report studies and lack protection from bias. Instead of trying to fix what is lacking and set better and clearer rules, one reaction is to overturn the tables and abolish any gatekeeping rules (such as removing the term statistical significance). However, potential for falsification is a prerequisite for science. Fields that obstinately resist refutation can hide behind the abolition of statistical significance but risk becoming self-ostracized from the remit of science.

Among the top-cited signatories who respond to their questionnaire, Hardwicke and Ioannidis find a heavy representation of fields with prevalent concerns about low reproducibility. Yet “abandoning the concept of statistical significance would make claims of ‘irreproducibility’ difficult if not impossible to make. In our opinion this approach may give bias a free pass”.

I agree, and will show why.

I continue with (excerpts of a preprint of) my article; references are formatted in the usual way. You can read the “free access” version here.

….

It might be assumed I would agree to “retire significance” since I often claim “the crude dichotomy of ‘pass/fail’ or ‘significant or not’ will scarcely do” and because I reformulate tests so as to “determine the magnitudes (and directions) of any statistical discrepancies warranted, and the limits to any substantive claims you may be entitled to infer from the statistical ones.”(Mayo 2018) [Genuine effects, as Fisher insisted,require not isolated small P-values, but a reliable method to successfully generate them.] We should not confuse prespecifying minimal thresholds in each test, which I would uphold, with fixing a value to habitually use (which I would not). N-P tests called for the practitioner to balance error probabilities according to context, not rigidly fix a value like .05. Nor does having a minimal P-value threshold mean we do not report the attained P-value: we should, and N-P agreed!

The “no threshold” view is not merely to never use the S word and report continuous P-values

These two rules alone would not lead Hardwicke and Ioannidis to charge, correctly, in my judgment, that “this approach may give bias a free pass”. ASA II and AGM decry using any prespecified P-value threshold as the basis for categorizing data in some way, such as inferring that results are, or are not, evidence of a genuine effect.

  • “Decisions to interpret or to publish results will not be based on statistical thresholds” (AGM).
  • “Whether a p-value passes any arbitrary threshold should not be considered at all” in interpreting data (ASA II).

Consider how far reaching the “no threshold” view is for interpreting data. For example, according to ASA II, in order for the U.S. Food and Drug Administration (FDA) to comply with its “no threshold” position, it does not suffice that they report continuous P-values and confidence intervals. The FDA would have to end its “long established drug review procedures that involve comparing p-values to significance thresholds for Phase III drug trials”.

The New England Journal of Medicine (NEJM) responds (2019)  to the ASA call to revise their guidelines, but insists that a central premise on which their revisions are based is “the use of statistical thresholds for claiming an effect or association should be limited to analyses for which the analysis plan outlined a method for controlling type I error”. In the article accompanying the revised guidelines:

“A well-designed randomized or observational study will have a primary hypothesis and a prespecified method of analysis, and the significance level from that analysis is a reliable indicator of the extent to which the observed data contradict a null hypothesis of no association between an intervention or an exposure and a response. Clinicians and regulatory agencies must make decisions about which treatment to use or to allow to be marketed, and P values interpreted by reliably calculated thresholds subjected to appropriate adjustments [for multiple trials] have a role in those decisions”.

Specifying “thresholds that have a strong theoretical and empirical justification” escapes the ASA II ruling: “Don’t conclude anything about scientific …importance based on statistical significance”.

Although less well advertised, the “no thresholds” view also torpedoes common uses of confidence intervals and Bayes Factor standards.

[T]he problem is not that of having only two labels. Results should not be trichotomized, or indeed categorized into any number of groups. Similarly, we need to stop using confidence intervals [CIs] as another means of dichotomizing. (ASA II)

AGM’s “compatibility intervals” are redolent of the consonance intervals of Kempthorne and Folks(1971) , except that the latter use many thresholds, one for each of several consonance levels. Even these would seem to violate the rule that results should not be “categorized into any number of groups”.

…Nor could Bayes factor thresholds be used, as they often are, to test a null against an alternative. It is not clear how any statistical tests survive. A claim has not passed a genuine test, if none of the results are allowed to count against it. We are not told what happens to the use of significance tests to check if statistical model assumptions hold approximately, or not–essential across methodologies. As George Box, a Bayesian, remarks, “diagnostic checks and tests of fit … require frequentist theory significance tests for their formal justification”(1983, p. 57).

What arguments are given to accept the no threshold view?

Getting past the appeals to popularity and fear, the reasons ASA II and AGM give are that thresholds can lead to well-known fallacies, and even to some howlers more extreme than those long lampooned. Of course it’s true:

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 …). Nor do statistically significant results ‘prove’ some other hypothesis. (AGM) 

It is easy to be swept up in their outrage, but the argument: “significance thresholds can be used very badly, therefore remove significance thresholds” is a very bad argument. Moreover, it would remove the very standards we need to call out the fallacies. A rule that went from any non-significant result to inferring no effect was proved, or to take something less extreme, to inferring it is well warranted or the like, would have extremely high Type II error probabilities.  They deal with a point null hypothesis, which makes it even worse.

The “free access” version is here.

Giving Data Dredgers a Free Pass

The danger of removing thresholds on grounds they could be badly used is that they are not there when you need them. Ioannidis zeroes in on the problem:

The proposal to entirely remove the barrier does not mean that scientists will not often still wish to interpret their results as showing important signals and fit preconceived notions and biases. With the gatekeeper of statistical significance, eager investigators whose analyses yield, for example, P = .09 have to either manipulate their statistics to get to P < .05 or add spin to their interpretation to suggest that results point to an important signal through an observed “trend.” When that gate keeper is removed, any result may be directly claimed to reflect an important signal or fit to a preexisting narrative.

As against Ioannidis’ anything goes charge, it might be said that even in a world without thresholds a largish P-value could not be taken as evidence of a genuine effect. For to do so would be to say something nonsensical. It would be to say: Even though larger differences would frequently be expected by chance variability alone (i.e., even though the P-value is largish), I maintain the data provide evidence they are not due to chance variability.

But such a response turns on appealing to a threshold to block it, minimally requiring the P-value be rather small e.g., < .1? (It also shows why P-values are apt measures for the job of distinguishing random error.) Thus, our eager investigators, facing a non-small P-value, are still incentivized to manipulate their statistics. Say they ransack the data until finding a non-prespecified subgroup that provides a nominally small enough P-value. In a world without thresholds, we would be hamstrung from highlighting, critically, P-values that breach (as opposed to uphold) preset thresholds.

“Whether a p-value passes any arbitrary threshold should not be considered at all when deciding which results to present or highlight” (my emphasis, ASA II).

More important than keeping a specific word is keeping a filter for error control. The 2016 ASA I warned in Principle 4: “Valid scientific conclusions based on p-values and related statistics cannot be drawn without at least knowing how many and which analyses were conducted, and how those analyses (including p-values) were selected for reporting”. …An unanswered question is how Principle 4 is to operate in a world with ASA II.

The NEJM’s revised guidelines, far from agreeing to use P-values without error probability thresholds, will now be stricter in their use. When no method to adjust for multiplicity of inferences or controlling the Type I error probability is prespecified, the report of secondary endpoints

should be limited to point estimates of treatment effects with 95% confidence intervals. In such cases, the Methods section should note that the widths of the intervals have not been adjusted for multiplicity and that the inferences drawn may not be reproducible. No P values should be reported for these analyses.

Confidence intervals severed from their dualities with tests, from which they were initially developed, lose their error probability guarantees.

Conclusion

The ASA P-value project is lately careering into recommendations on which there has been little balanced discussion and much disagreement. Hardwicke and Ioannidis find that more than half of the respondents deny significance should be excluded from all science, and the 43 papers in the special issue “Moving to a world beyond ‘p < 0.05’” offer a cacophony of competing reforms.

It is hard to resist the missionary zeal of masterful calls: Do you want bad science to thrive? or Do you want to ban significance? (a false dilemma). A question to raise before jumping on the bandwagon: Are they asking the most unbiased questions about the consequences of removing thresholds currently ensconced into hundreds of legal statutes and best practice manuals? This needs to be carefully considered, if the reforms intended to improve credibility of statistics are not to backfire, as they may already be doing.

ASA II is part of a large undertaking; it contains plenty of sagacious advice. Notably the M in ATOM: Modesty.

Be modest by recognizing that different readers may have very different stakes on the results of your analysis, which means you should try to take the role of a neutral judge rather than an advocate for any hypothesis.

ASA II regards its positions “open to debate”. An open debate is very much needed.

Here’s the full (uncorrected) preprint of my editorial.

*Mayo (2018), Mayo and Cox (2006), Mayo and Spanos (2006).

Acknowledgement

I would like to thank D. Hand, N. Schachtman and A. Spanos for comments and corrections on earlier drafts.

References not linked above

Birnbaum, A. Statistical Methods in Scientific Inference (letter to the Editor), Nature 1970;225(5237):1033.

Box, G. An apology for ecumenism in statistics. In G. E. P. Box, T. Leonard, and D. F. J. Wu (Eds.), Scientific inference, data analysis, and robustness. Academic Press, 1983:51-84.

Fisher, RA. The design of experiments, Oliver and Boyd, 1947.

Kempthorne, O, Folks, J. Probability, statistics, data analysis. Iowa State University Press, 1971.

Mayo, D. G. Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars. CUP, 2018.

Mayo, D.G. and Cox, D. R. “Frequentist Statistics as a Theory of Inductive Inference,” Optimality: The Second Erich L. Lehmann Symposium (ed. J. Rojo), Lecture Notes-Monograph series, Institute of Mathematical Statistics (IMS), 2006; 49: 77-97.

Mayo, D. G. and Spanos, A. “Severe Testing as a Basic Concept in a Neyman-Pearson Philosophy of Induction,” British Journal of Philosophy of Science, 2006;57: 323-357.

Neyman, J. Tests of statistical hypotheses and their use in studies of natural phenomena. Communications in Statistics: Theory and Methods 1976;5(8):737–51.

NEJM Author Guidelines: Retrieved from: https://www.nejm.org/author-center/new-manuscripts on July 19, 2019.

 

Relevant (2019) posts:

The 2019 ASA Guide to P-values and Significance: Don’t say What You don’t Mean (Some Recommendations)

The NEJM Issues New Guidelines on Statistical Reporting: Is the ASA P-Value Project Backfiring?

 

 

 

Categories: ASA Guide to P-values, P-values, stat wars and their casualties | 4 Comments

Gelman blogged our exchange on abandoning statistical significance

A. Gelman

I came across this post on Gelman’s blog today:

Exchange with Deborah Mayo on abandoning statistical significance

It was straight out of blog comments and email correspondence back when the ASA, and significant others, were rising up against the concept of statistical significance. Here it is:

Exchange with Deborah Mayo on abandoning statistical significance

The philosopher wrote:

The big move in the statistics wars these days is to fight irreplication by making it harder to reject, and find evidence against, a null hypothesis.

Mayo is referring to, among other things, the proposal to “redefine statistical significance” as p less than 0.005. My colleagues and I do not actually like that idea, so I responded to Mayo as follows:

I don’t know what the big moves are, but my own perspective, and I think that of the three authors of the recent article being discussed, is that we should not be “rejecting” at all, that we should move beyond the idea that the purpose of statistics is to reject the null hypothesis of zero effect and zero systematic error.

I don’t want to ban speech, and I don’t think the authors of that article do, either. I’m on record that I’d like to see everything published, including Bem’s ESP paper data and various other silly research. My problem is with the idea that rejecting the null hypothesis tells us anything useful.

Mayo replied:

I just don’t see that you can really mean to say that nothing is learned from finding low-p values, especially if it’s not an isolated case but time and again. We may know a hypothesis/model is strictly false, but we do not yet know in which way we will find violations. Otherwise we could never learn from data. As a falsificationist, you must think we find things out from discovering our theory clashes with the facts–enough even to direct a change in your model. Even though inferences are strictly fallible, we may argue from coincidence to a genuine anomaly & even to pinpointing the source of the misfit.So I’m puzzled.
I hope that “only” will be added to the statement in the editorial to the ASA collection. Doesn’t the ASA worry that the whole effort might otherwise be discredited as anti-science?

My response:

The problem with null hypothesis significance testing is that rejection of straw-man hypothesis B is used as evidence in favor of preferred alternative A. This is a disaster. See here.

Then Mayo:

I know all this. I’ve been writing about it for donkey’s years. But that’s a testing fallacy. N-P and Fisher couldn’t have been clearer. That does not mean we learn nothing from a correct use of tests. N-P tests have a statistical alternative and at most one learns, say, about a discrepancy from a hypothesized value. If a double blind RCT clinical trial repeatedly shows statistically significant (small p-value) increase in cancer risks among exposed, will you deny that’s evidence?

Me:

I don’t care about the people, Neyman, Fisher, and Pearson. I care about what researchers do. They do something called NHST, and it’s a disaster, and I’m glad that Greenland and others are writing papers pointing this out.

Mayo:

We’ve been saying this for years and years. Are you saying you would no longer falsify models because some people will move from falsifying a model to their favorite alternative theory that fits the data? That’s crazy. You don’t give up on correct logic because some people use illogic. The clinical trials I’m speaking about do not commit those crimes. would you really be willing to say that they’re all bunk because some psychology researchers do erroneous experiments and make inferences to claims where we don’t even know we’re measuring the intended phenomenon?
Ironically, by the way, the Greenland argument only weakens the possibility of finding failed replications.

Me:

I pretty much said it all here.

I don’t think clinical trials are all bunk. I think that existing methods, NHST included, can be adapted to useful purposes at times. But I think the principles underlying these methods don’t correspond to the scientific questions of interest, and I think there are lots of ways to do better.

Mayo:

And I’ve said it all many times in great detail. I say drop NHST. It was never part of any official methodology. That is no justification for endorsing official policy that denies we can learn from statistically significant effects in controlled clinical trials among other legitimate probes. Why not punish the wrong-doers rather than all of science that uses statistical falsification?

Would critics of statistical significance tests use a drug that resulted in statistically significant increased risks in patients time and again? Would they recommend it to members of their family? If the answer to these questions is “no”, then they cannot at the same time deny that anything can be learned from finding statistical significance.

Me:

In those cases where NHST works, I think other methods work better. To me, the main value of significance testing is: (a) when the test doesn’t reject, that tells you your data are too noisy to reject the null model, and so it’s good to know that, and (b) in some cases as a convenient shorthand for a more thorough analysis, and (3) for finding flaws in models that we are interested in (as in chapter 6 of BDA). I would not use significance testing to evaluate a drug, or to prove that some psychological manipulation has a nonzero effect, or whatever, and those are the sorts of examples that keep coming up.

In answer to your previous email, I don’t want to punish anyone, I just think statistical significance is a bad idea and I think we’d all be better off without it. In your example of a drug, the key phrase is “time and again.” No statistical significance is needed here.

Mayo:

One or two times would be enough if they were well controlled. And the ONLY reason they have meaning even if it were time and time again is because they are well controlled. I’m totally puzzled as to how you can falsify models using p-values & deny p-value reasoning.

As I discuss through my book, Statistical Inference as Severe Testing, the most important role of the severity requirement is to block claims—precisely the kinds of claims that get support under other methods be they likelihood or Bayesian.
Stop using NHST—there’s speech ban I can agree with. In many cases the best way to evaluate a drug is via controlled trials. I think you forget that for me, since any claim must be well probed to be warranted, estimations can still be viewed as tests.
I will stop trading in biotechs if the rule to just report observed effects gets passed and the responsibility that went with claiming a genuinely statistically significant effect goes by the board.

That said, it’s fun to be talking with you again.

Me:

I’m interested in falsifying real models, not straw-man nulls of zero effect. Regarding your example of the new drug: yes, it can be solved using confidence intervals, or z-scores, or estimates and standard errors, or p-values, or Bayesian methods, or just about anything, if the evidence is strong enough. I agree there are simple problems for which many methods work, including p-values when properly interpreted. But I don’t see the point of using hypothesis testing in those situations either—it seems to make much more sense to treat them as estimation problems: how effective is the drug, ideally for each person or else just estimate the average effect if you’re ok fitting that simpler model.

I can blog our exchange if you’d like.

And so I did.

Please be polite in any comments. Thank you.

I was glad to see that I’d pretty much said just what I’d want to say. I might have wanted to get the last word in regarding his last remark, namely I would say that I think the task of distinguishing genuine from spurious effects is crucial. If you start out thinking you’re “estimating” something when it could readily have been exposed as noise, you will be led astray. The only confusion in what I’d said might be as regards the term “NHST”. On this, see comments to this post and my “Farewell Keepsake” from SIST (2018, CUP)

Categories: Gelman blogs an exchange with Mayo | Tags: | 7 Comments

All She Wrote (so far): Error Statistics Philosophy: 8 years on

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Error Statistics Philosophy: Blog Contents (8 years)
By: D. G. Mayo

Dear Reader: I began this blog 8 years ago (Sept. 3, 2011)! A double celebration is taking place at the Elbar Room Friday evening (a smaller one was held earlier in the week), both for the blog and the 1 year anniversary of the physical appearance of my book: Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars [SIST] (CUP). A special rush edition made an appearance on Sept 3, 2018 in time for the RSS meeting in Cardiff. If you’re in the neighborhood, stop by for some Elba Grease.

Ship Statinfasst made its most recent journey at the Summer Seminar for Phil Stat from July 28-Aug 11, co-directed with Aris Spanos. It was one of the main events that occupied my time the past academic year, from the planning, advertising and running. We had 15 fantastic faculty and post-doc participants (from 55 applicants), and plan to continue the movement to incorporate PhilStat in philosophy and methodology, both in teaching and research. You can find slides from the Seminar (zoom videos, including those of special invited speakers, to come) on SummerSeminarPhilStat.com. Slides and other materials from the Spring Seminar co-taught with Aris Spanos (and cross-listed with Economics) can be found on this blog here

Continue reading

Categories: 8 year memory lane, blog contents, Metablog | 3 Comments

(one year ago) RSS 2018 – Significance Tests: Rethinking the Controversy

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Here’s what I posted 1 year ago on Aug 30, 2018.

 

Day 2, Wednesday 05/09/2018

11:20 – 13:20

Keynote 4 – Significance Tests: Rethinking the Controversy Assembly Room

Speakers:
Sir David Cox, Nuffield College, Oxford
Deborah Mayo, Virginia Tech
Richard Morey, Cardiff University
Aris Spanos, Virginia Tech

Intermingled in today’s statistical controversies are some long-standing, but unresolved, disagreements on the nature and principles of statistical methods and the roles for probability in statistical inference and modelling. In reaction to the so-called “replication crisis” in the sciences, some reformers suggest significance tests as a major culprit. To understand the ramifications of the proposed reforms, there is a pressing need for a deeper understanding of the source of the problems in the sciences and a balanced critique of the alternative methods being proposed to supplant significance tests. In this session speakers offer perspectives on significance tests from statistical science, econometrics, experimental psychology and philosophy of science. There will be also be panel discussion.

Categories: memory lane | Tags: | Leave a comment

Palavering about Palavering about P-values

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Nathan Schachtman (who was a special invited speaker at our recent Summer Seminar in Phil Stat) put up a post on his law blog the other day (“Palavering About P-values”) on an article by a statistics professor at Stanford, Helena Kraemer. “Palavering” is an interesting word choice of Schachtman’s. Its range of meanings is relevant here [i]; in my title, I intend both, in turn. You can read Schachtman’s full post here, it begins like this:

The American Statistical Association’s most recent confused and confusing communication about statistical significance testing has given rise to great mischief in the world of science and science publishing.[ASA II 2019] Take for instance last week’s opinion piece about “Is It Time to Ban the P Value?” Please.

Admittedly, their recent statement, which I refer to as ASA II, has seemed to open the floodgates to some very zany remarks about P-values, their meaning and role in statistical testing. Continuing with Schachtman’s post: Continue reading

Categories: ASA Guide to P-values, P-values | Tags: | 12 Comments

A. Spanos: Egon Pearson’s Neglected Contributions to Statistics

Continuing with posts on E.S. Pearson in marking his birthday:

Egon Pearson’s Neglected Contributions to Statistics

by Aris Spanos

    Egon Pearson (11 August 1895 – 12 June 1980), is widely known today for his contribution in recasting of Fisher’s significance testing into the Neyman-Pearson (1933) theory of hypothesis testing. Occasionally, he is also credited with contributions in promoting statistical methods in industry and in the history of modern statistics; see Bartlett (1981). What is rarely mentioned is Egon’s early pioneering work on:

(i) specification: the need to state explicitly the inductive premises of one’s inferences,

(ii) robustness: evaluating the ‘sensitivity’ of inferential procedures to departures from the Normality assumption, as well as

(iii) Mis-Specification (M-S) testing: probing for potential departures from the Normality  assumption.

Arguably, modern frequentist inference began with the development of various finite sample inference procedures, initially by William Gosset (1908) [of the Student’s t fame] and then Fisher (1915, 1921, 1922a-b). These inference procedures revolved around a particular statistical model, known today as the simple Normal model:

Xk ∽ NIID(μ,σ²), k=1,2,…,n,…             (1)

where ‘NIID(μ,σ²)’ stands for ‘Normal, Independent and Identically Distributed with mean μ and variance σ²’. These procedures include the ‘optimal’ estimators of μ and σ², Xbar and s², and the pivotal quantities:

(a) τ(X) =[√n(Xbar- μ)/s] ∽ St(n-1),  (2)

(b) v(X) =[(n-1)s²/σ²] ∽ χ²(n-1),        (3)

where St(n-1) and χ²(n-1) denote the Student’s t and chi-square distributions with (n-1) degrees of freedom. Continue reading

Categories: Egon Pearson, Statistics | Leave a comment

Statistical Concepts in Their Relation to Reality–E.S. Pearson

11 August 1895 – 12 June 1980

In marking Egon Pearson’s birthday (Aug. 11), I’ll  post some Pearson items this week. They will contain some new reflections on older Pearson posts on this blog. Today, I’m posting “Statistical Concepts in Their Relation to Reality” (Pearson 1955). I’ve linked to it several times over the years, but always find a new gem or two, despite its being so short. E. Pearson rejected some of the familiar tenets that have come to be associated with Neyman and Pearson (N-P) statistical tests, notably the idea that the essential justification for tests resides in a long-run control of rates of erroneous interpretations–what he termed the “behavioral” rationale of tests. In an unpublished letter E. Pearson wrote to Birnbaum (1974), he talks about N-P theory admitting of two interpretations: behavioral and evidential:

“I think you will pick up here and there in my own papers signs of evidentiality, and you can say now that we or I should have stated clearly the difference between the behavioral and evidential interpretations. Certainly we have suffered since in the way the people have concentrated (to an absurd extent often) on behavioral interpretations”.

(Nowadays, it might be said that some people concentrate to an absurd extent on “science-wise error rates” in their view of statistical tests as dichotomous screening devices.) Continue reading

Categories: Egon Pearson, phil/history of stat, Philosophy of Statistics | Tags: , , | Leave a comment

Performance or Probativeness? E.S. Pearson’s Statistical Philosophy: Belated Birthday Wish

E.S. Pearson

This is a belated birthday post for E.S. Pearson (11 August 1895-12 June, 1980). It’s basically a post from 2012 which concerns an issue of interpretation (long-run performance vs probativeness) that’s badly confused these days. I’ll post some Pearson items this week to mark his birthday.

HAPPY BELATED BIRTHDAY EGON!

Are methods based on error probabilities of use mainly to supply procedures which will not err too frequently in some long run? (performance). Or is it the other way round: that the control of long run error properties are of crucial importance for probing the causes of the data at hand? (probativeness). I say no to the former and yes to the latter. This, I think, was also the view of Egon Sharpe (E.S.) Pearson. 

Cases of Type A and Type B

“How far then, can one go in giving precision to a philosophy of statistical inference?” (Pearson 1947, 172)

Pearson considers the rationale that might be given to N-P tests in two types of cases, A and B:

“(A) At one extreme we have the case where repeated decisions must be made on results obtained from some routine procedure…

(B) At the other is the situation where statistical tools are applied to an isolated investigation of considerable importance…?” (ibid., 170)

Continue reading

Categories: E.S. Pearson, Error Statistics | Leave a comment

S. Senn: Red herrings and the art of cause fishing: Lord’s Paradox revisited (Guest post)

 

Stephen Senn
Consultant Statistician
Edinburgh

Background

Previous posts[a],[b],[c] of mine have considered Lord’s Paradox. To recap, this was considered in the form described by Wainer and Brown[1], in turn based on Lord’s original formulation:

A large university is interested in investigating the effects on the students of the diet provided in the university dining halls : : : . Various types of data are gathered. In particular, the weight of each student at the time of his arrival in September and his weight the following June are recorded. [2](p. 304)

The issue is whether the appropriate analysis should be based on change-scores (weight in June minus weight in September), as proposed by a first statistician (whom I called John) or analysis of covariance (ANCOVA), using the September weight as a covariate, as proposed by a second statistician (whom I called Jane). There was a difference in mean weight between halls at the time of arrival in September (baseline) and this difference turned out to be identical to the difference in June (outcome). It thus follows that, since the analysis of change score is algebraically equivalent to correcting the difference between halls at outcome by the difference between halls at baseline, the analysis of change scores returns an estimate of zero. The conclusion is thus, there being no difference between diets, diet has no effect. Continue reading

Categories: Stephen Senn | 24 Comments

Summer Seminar in PhilStat Participants and Special Invited Speakers

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Participants in the 2019 Summer Seminar in Philosophy of Statistics

Continue reading

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The NEJM Issues New Guidelines on Statistical Reporting: Is the ASA P-Value Project Backfiring? (i)

The New England Journal of Medicine NEJM announced new guidelines for authors for statistical reporting  yesterday*. The ASA describes the change as “in response to the ASA Statement on P-values and Statistical Significance and subsequent The American Statistician special issue on statistical inference” (ASA I and II, in my abbreviation). If so, it seems to have backfired. I don’t know all the differences in the new guidelines, but those explicitly noted appear to me to move in the reverse direction from where the ASA I and II guidelines were heading.

The most notable point is that the NEJM highlights the need for error control, especially for constraining the Type I error probability, and pays a lot of attention to adjusting P-values for multiple testing and post hoc subgroups. ASA I included an important principle (#4) that P-values are altered and may be invalidated by multiple testing, but they do not call for adjustments for multiplicity, nor do I find a discussion of Type I or II error probabilities in the ASA documents. NEJM gives strict requirements for controlling family-wise error rate or false discovery rates (understood as the Benjamini and Hochberg frequentist adjustments). Continue reading

Categories: ASA Guide to P-values | 21 Comments

B. Haig: The ASA’s 2019 update on P-values and significance (ASA II)(Guest Post)

Brian Haig, Professor Emeritus
Department of Psychology
University of Canterbury
Christchurch, New Zealand

The American Statistical Association’s (ASA) recent effort to advise the statistical and scientific communities on how they should think about statistics in research is ambitious in scope. It is concerned with an initial attempt to depict what empirical research might look like in “a world beyond p<0.05” (The American Statistician, 2019, 73, S1,1-401). Quite surprisingly, the main recommendation of the lead editorial article in the Special Issue of The American Statistician devoted to this topic (Wasserstein, Schirm, & Lazar, 2019; hereafter, ASA II) is that “it is time to stop using the term ‘statistically significant’ entirely”. (p.2) ASA II acknowledges the controversial nature of this directive and anticipates that it will be subject to critical examination. Indeed, in a recent post, Deborah Mayo began her evaluation of ASA II by making constructive amendments to three recommendations that appear early in the document (‘Error Statistics Philosophy’, June 17, 2019). These amendments have received numerous endorsements, and I record mine here. In this short commentary, I briefly state a number of general reservations that I have about ASA II. Continue reading

Categories: ASA Guide to P-values, Brian Haig | Tags: | 31 Comments

SIST: All Excerpts and Mementos: May 2018-July 2019 (updated)

Introduction & Overview

The Meaning of My Title: Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars* 05/19/18

Blurbs of 16 Tours: Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (SIST) 03/05/19

 

Excursion 1

EXCERPTS

Tour I Ex1 TI (full proofs)

Excursion 1 Tour I: Beyond Probabilism and Performance: Severity Requirement (1.1) 09/08/18

Excursion 1 Tour I (2nd stop): Probabilism, Performance, and Probativeness (1.2) 09/11/18

Excursion 1 Tour I (3rd stop): The Current State of Play in Statistical Foundations: A View From a Hot-Air Balloon (1.3) 09/15/18

Tour II

Excursion 1 Tour II: Error Probing Tools versus Logics of Evidence-Excerpt 04/04/19

Souvenir C: A Severe Tester’s Translation Guide (Excursion 1 Tour II) 11/08/18

MEMENTOS

Tour Guide Mementos (Excursion 1 Tour II of How to Get Beyond the Statistics Wars) 10/29/18

 

Excursion 2

EXCERPTS

Tour I

Excursion 2: Taboos of Induction and Falsification: Tour I (first stop) 09/29/18

“It should never be true, though it is still often said, that the conclusions are no more accurate than the data on which they are based” (Keepsake by Fisher, 2.1) 10/05/18

Tour II

Excursion 2 Tour II (3rd stop): Falsification, Pseudoscience, Induction (2.3) 10/10/18

MEMENTOS

Tour Guide Mementos and Quiz 2.1 (Excursion 2 Tour I Induction and Confirmation) 11/14/18

Mementos for Excursion 2 Tour II Falsification, Pseudoscience, Induction 11/17/18

 

Excursion 3

EXCERPTS

Tour I

Where are Fisher, Neyman, Pearson in 1919? Opening of Excursion 3 11/30/18

Neyman-Pearson Tests: An Episode in Anglo-Polish Collaboration: Excerpt from Excursion 3 (3.2) 12/01/18

First Look at N-P Methods as Severe Tests: Water plant accident [Exhibit (i) from Excursion 3] 12/04/18

Tour II

It’s the Methods, Stupid: Excerpt from Excursion 3 Tour II (Mayo 2018, CUP) 12/11/18

60 Years of Cox’s (1958) Chestnut: Excerpt from Excursion 3 tour II. 12/29/18

Tour III

Capability and Severity: Deeper Concepts: Excerpts From Excursion 3 Tour III 12/20/18

MEMENTOS

Memento & Quiz (on SEV): Excursion 3, Tour I 12/08/18

Mementos for “It’s the Methods, Stupid!” Excursion 3 Tour II (3.4-3.6) 12/13/18

Tour Guide Mementos From Excursion 3 Tour III: Capability and Severity: Deeper Concepts 12/26/18

 

Excursion 4

EXCERPTS

Tour I

Excerpt from Excursion 4 Tour I: The Myth of “The Myth of Objectivity” (Mayo 2018, CUP) 12/26/18

Tour II

Excerpt from Excursion 4 Tour II: 4.4 “Do P-Values Exaggerate the Evidence?” 01/10/19
(Full Excursion 4 Tour II)

Tour IV

Excerpt from Excursion 4 Tour IV: More Auditing: Objectivity and Model Checking 01/27/19

MEMENTOS

Mementos from Excursion 4: Blurbs of Tours I-IV 01/13/19

 

Excursion 5

Tour I

(Full) Excerpt: Excursion 5 Tour I — Power: Pre-data and Post-data (from “SIST: How to Get Beyond the Stat Wars”) 04/27/19

Tour II

(Full) Excerpt. Excursion 5 Tour II: How Not to Corrupt Power (Power Taboos, Retro Power, and Shpower) 06/07/19

Tour III

Deconstructing the Fisher-Neyman conflict wearing Fiducial glasses + Excerpt 5.8 from SIST 02/23/19

 

Excursion 6

Tour I Ex6 TI What Ever Happened to Bayesian Foundations?

Tour II

Excerpts: Souvenir Z: Understanding Tribal Warfare +  6.7 Farewell Keepsake from SIST + List of Souvenirs 05/04/19

 

*Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (Mayo, CUP 2018).

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The Statistics Wars: Errors and Casualties

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Had I been scheduled to speak later at the 12th MuST Conference & 3rd Workshop “Perspectives on Scientific Error” in Munich, rather than on day 1, I could have (constructively) illustrated some of the errors and casualties by reference to a few of the conference papers that discussed significance tests. (Most gave illuminating discussions of such topics as replication research, the biases that discredit meta-analysis, statistics in the law, formal epistemology [i]). My slides follow my abstract. Continue reading

Categories: slides, stat wars and their casualties | Tags: | Leave a comment

“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 | 94 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

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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

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SIST: All Excerpts and Mementos: May 2018-May 2019

view from a hot-air balloon

Introduction & Overview

The Meaning of My Title: Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars* 05/19/18

Blurbs of 16 Tours: Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (SIST) 03/05/19

 

Excursion 1

EXCERPTS

Tour I

Excursion 1 Tour I: Beyond Probabilism and Performance: Severity Requirement (1.1) 09/08/18

Excursion 1 Tour I (2nd stop): Probabilism, Performance, and Probativeness (1.2) 09/11/18

Excursion 1 Tour I (3rd stop): The Current State of Play in Statistical Foundations: A View From a Hot-Air Balloon (1.3) 09/15/18

Tour II

Excursion 1 Tour II: Error Probing Tools versus Logics of Evidence-Excerpt 04/04/19

Souvenir C: A Severe Tester’s Translation Guide (Excursion 1 Tour II) 11/08/18

MEMENTOS

Tour Guide Mementos (Excursion 1 Tour II of How to Get Beyond the Statistics Wars) 10/29/18

 

Excursion 2

EXCERPTS

Tour I

Excursion 2: Taboos of Induction and Falsification: Tour I (first stop) 09/29/18

“It should never be true, though it is still often said, that the conclusions are no more accurate than the data on which they are based” (Keepsake by Fisher, 2.1) 10/05/18

Tour II

Excursion 2 Tour II (3rd stop): Falsification, Pseudoscience, Induction (2.3) 10/10/18

MEMENTOS

Tour Guide Mementos and Quiz 2.1 (Excursion 2 Tour I Induction and Confirmation) 11/14/18

Mementos for Excursion 2 Tour II Falsification, Pseudoscience, Induction 11/17/18

 

Excursion 3

EXCERPTS

Tour I

Where are Fisher, Neyman, Pearson in 1919? Opening of Excursion 3 11/30/18

Neyman-Pearson Tests: An Episode in Anglo-Polish Collaboration: Excerpt from Excursion 3 (3.2) 12/01/18

First Look at N-P Methods as Severe Tests: Water plant accident [Exhibit (i) from Excursion 3] 12/04/18

Tour II

It’s the Methods, Stupid: Excerpt from Excursion 3 Tour II (Mayo 2018, CUP) 12/11/18

60 Years of Cox’s (1958) Chestnut: Excerpt from Excursion 3 tour II. 12/29/18

Tour III

Capability and Severity: Deeper Concepts: Excerpts From Excursion 3 Tour III 12/20/18

MEMENTOS

Memento & Quiz (on SEV): Excursion 3, Tour I 12/08/18

Mementos for “It’s the Methods, Stupid!” Excursion 3 Tour II (3.4-3.6) 12/13/18

Tour Guide Mementos From Excursion 3 Tour III: Capability and Severity: Deeper Concepts 12/26/18

 

Excursion 4

EXCERPTS

Tour I

Excerpt from Excursion 4 Tour I: The Myth of “The Myth of Objectivity” (Mayo 2018, CUP) 12/26/18

Tour II

Excerpt from Excursion 4 Tour II: 4.4 “Do P-Values Exaggerate the Evidence?” 01/10/19

Tour IV

Excerpt from Excursion 4 Tour IV: More Auditing: Objectivity and Model Checking 01/27/19

MEMENTOS

Mementos from Excursion 4: Blurbs of Tours I-IV 01/13/19

 

Excursion 5

Tour I

(full) Excerpt: Excursion 5 Tour I — Power: Pre-data and Post-data (from “SIST: How to Get Beyond the Stat Wars”) 04/27/19

Tour III

Deconstructing the Fisher-Neyman conflict wearing Fiducial glasses + Excerpt 5.8 from SIST 02/23/19

 

Excursion 6

Tour II

Excerpts: Souvenir Z: Understanding Tribal Warfare +  6.7 Farewell Keepsake from SIST + List of Souvenirs 05/04/19

*Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (Mayo, CUP 2018).

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Excerpts: Final Souvenir Z, Farewell Keepsake & List of Souvenirs

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We’ve reached our last Tour (of SIST)*: Pragmatic and Error Statistical Bayesians (Excursion 6), marking the end of our reading with Souvenir Z, the final Souvenir, as well as the Farewell Keepsake in 6.7. Our cruise ship Statinfasst, currently here at Thebes, will be back at dock for maintenance for our next launch at the Summer Seminar in Phil Stat (July 28-Aug 11). Although it’s not my preference that new readers begin with the Farewell Keepsake (it contains a few spoilers), I’m excerpting it together with Souvenir Z (and a list of all souvenirs A – Z) here, and invite all interested readers to peer in. There’s a check list on p. 437: If you’re in the market for a new statistical account, you’ll want to test if it satisfies the items on the list. Have fun!

Souvenir Z: Understanding Tribal Warfare

We began this tour asking: Is there an overarching philosophy that “matches contemporary attitudes”? More important is changing attitudes. Not to encourage a switch of tribes, or even a tribal truce, but something more modest and actually achievable: to understand and get beyond the tribal warfare. To understand them, at minimum, requires grasping how the goals of probabilism differ from those of probativeness. This leads to a way of changing contemporary attitudes that is bolder and more challenging. Snapshots from the error statistical lens let you see how frequentist methods supply tools for controlling and assessing how well or poorly warranted claims are. All of the links, from data generation to modeling, to statistical inference and from there to substantive research claims, fall into place within this statistical philosophy. If this is close to being a useful way to interpret a cluster of methods, then the change in contemporary attitudes is radical: it has never been explicitly unveiled. Our journey was restricted to simple examples because those are the ones fought over in decades of statistical battles. Much more work is needed. Those grappling with applied problems are best suited to develop these ideas, and see where they may lead. I never promised,when you bought your ticket for this passage, to go beyond showing that viewing statistics as severe testing will let you get beyond the statistics wars.

6.7 Farewell Keepsake

Despite the eclecticism of statistical practice, conflicting views about the roles of probability and the nature of statistical inference – holdovers from long-standing frequentist–Bayesian battles – still simmer below the surface of today’s debates. Reluctance to reopen wounds from old battles has allowed them to fester. To assume all we need is an agreement on numbers – even if they’re measuring different things – leads to statistical schizophrenia. Rival conceptions of the nature of statistical inference show up unannounced in the problems of scientific integrity, irreproducibility, and questionable research practices, and in proposed methodological reforms. If you don’t understand the assumptions behind proposed reforms, their ramifications for statistical practice remain hidden from you.

Rival standards reflect a tension between using probability (a) to constrain the probability that a method avoids erroneously interpreting data in a series of applications (performance), and (b) to assign degrees of support, confirmation, or plausibility to hypotheses (probabilism). We set sail on our journey with an informal tool for telling what’s true about statistical inference: If little if anything has been done to rule out flaws in taking data as evidence for a claim, then that claim has not passed a severe test . From this minimal severe-testing requirement, we develop a statistical philosophy that goes beyond probabilism and performance. The goals of the severe tester (probativism) arise in contexts sufficiently different from those of probabilism that you are free to hold both, for distinct aims (Section 1.2). For statistical inference in science, it is severity we seek. A claim passes with severity only to the extent that it is subjected to, and passes, a test that it probably would have failed, if false. Viewing statistical inference as severe testing alters long-held conceptions of what’s required for an adequate account of statistical inference in science. In this view, a normative statistical epistemology –  an account of what’ s warranted to infer –  must be:

  directly altered by biasing selection effects
  able to falsify claims statistically
  able to test statistical model assumptions
  able to block inferences that violate minimal severity

These overlapping and interrelated requirements are disinterred over the course of our travels. This final keepsake collects a cluster of familiar criticisms of error statistical methods. They are not intended to replace the detailed arguments, pro and con, within; here we cut to the chase, generally keeping to the language of critics. Given our conception of evidence, we retain testing language even when the statistical inference is an estimation, prediction, or proposed answer to a question. The concept of severe testing is sufficiently general to apply to any of the methods now in use. It follows that a variety of statistical methods can serve to advance the severity goal, and that they can, in principle, find their foundations in an error statistical philosophy. However, each requires supplements and reformulations to be relevant to real-world learning. Good science does not turn on adopting any formal tool, and yet the statistics wars often focus on whether to use one type of test (or estimation, or model selection) or another. Meta-researchers charged with instigating reforms do not agree, but the foundational basis for the disagreement is left unattended. It is no wonder some see the statistics wars as proxy wars between competing tribe leaders, each keen to advance one or another tool, rather than about how to do better science. Leading minds are drawn into inconsequential battles, e.g., whether to use a prespecified cut-off  of 0.025 or 0.0025 –  when in fact good inference is not about cut-offs altogether but about a series of small-scale steps in collecting, modeling and analyzing data that work together to find things out. Still, we need to get beyond the statistics wars in their present form. By viewing a contentious battle in terms of a difference in goals –  finding highly probable versus highly well probed hypotheses – readers can see why leaders of rival tribes often talk past each other. To be clear, the standpoints underlying the following criticisms are open to debate; we’re far from claiming to do away with them. What should be done away with is rehearsing the same criticisms ad nauseum. Only then can we hear the voices of those calling for an honest standpoint about responsible science.

1. NHST Licenses Abuses. First, there’s the cluster of criticisms directed at an abusive NHST animal: NHSTs infer from a single P-value below an arbitrary cut-off to evidence for a research claim, and they encourage P-hacking, fishing, and other selection effects. The reply: this ignores crucial requirements set by Fisher and other founders: isolated significant results are poor evidence of a genuine effect and statistical significance doesn’t warrant substantive, (e.g., causal) inferences. Moreover, selective reporting invalidates error probabilities. Some argue significance tests are un-Popperian because the higher the sample size, the easier to infer one’s research hypothesis. It’s true that with a sufficiently high sample size any discrepancy from a null hypothesis has a high probability of being detected, but statistical significance does not license inferring a research claim H. Unless H’s errors have been well probed by merely finding a small P-value, H passes an extremely insevere test. No mountains out of molehills (Sections 4.3 and 5.1). Enlightened users of statistical tests have rejected the cookbook, dichotomous NHST, long lampooned: such criticisms are behind the times. When well-intentioned aims of replication research are linked to these retreads, it only hurts the cause. One doesn’t need a sharp dichotomy to identify rather lousy tests – a main goal for a severe tester. Granted, policy-making contexts may require cut-offs, as do behavioristic setups. But in those contexts, a test’s error probabilities measure overall error control, and are not generally used to assess well-testedness. Even there, users need not fall into the NHST traps (Section 2.5). While attention to banning terms is the least productive aspect of the statistics wars, since NHST is not used by Fisher or N-P, let’s give the caricature its due and drop the NHST acronym; “statistical tests” or “error statistical tests” will do. Simple significance tests are a small part of a conglomeration of error statistical methods.

To continue reading: Excerpt Souvenir Z, Farewell Keepsake & List of Souvenirs can be found here.

*We are reading Statistical Inference as Severe Testing: How to Get beyond the Statistics Wars (2018, CUP)

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Where YOU are in the journey.

 


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(full) Excerpt: Excursion 5 Tour I — Power: Pre-data and Post-data (from “SIST: How to Get Beyond the Stat Wars”)

S.S. StatInfasST

It’s a balmy day today on Ship StatInfasST: An invigorating wind has a salutary effect on our journey. So, for the first time I’m excerpting all of Excursion 5 Tour I (proofs) of Statistical Inference as Severe Testing How to Get Beyond the Statistics Wars (2018, CUP)

A salutary effect of power analysis is that it draws one forcibly to consider the magnitude of effects. In psychology, and especially in soft psychology, under the sway of the Fisherian scheme, there has been little consciousness of how big things are. (Cohen 1990, p. 1309)

 So how would you use power to consider the magnitude of effects were you drawn forcibly to do so? In with your breakfast is an exercise to get us started on today’ s shore excursion.

Suppose you are reading about a statistically signifi cant result x (just at level α ) from a one-sided test T+ of the mean of a Normal distribution with IID samples, and known σ: H0 : μ ≤ 0 against H1 : μ > 0. Underline the correct word, from the perspective of the (error statistical) philosophy, within which power is defined.

  • If the test’ s power to detect μ′ is very low (i.e., POW(μ′ ) is low), then the statistically significant x is poor/good evidence that μ > μ′ .
  • Were POW(μ′ ) reasonably high, the inference to μ > μ′ is reasonably/poorly warranted.

Continue reading

Categories: Statistical Inference as Severe Testing, Statistical power | 1 Comment

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