Why are hypothesis tests (often) poorly explained as an “idiot’s guide”?

From Aris Spanos:

“Inadequate knowledge by textbook writers who often do not have the technical skills to read and understand the original sources, and have to rely on second hand accounts of previous textbook writers that are often misleading or just outright erroneous. In most of these textbooks hypothesis testing is poorly explained as an idiot’s guide to combining off-the-shelf formulae with statistical table.

“A deliberate attempt to distort and cannibalize frequentist testing for certain Bayesian statisticians who revel in (unfairly) maligning frequentist inference in their misguided attempt to motivate their preferred viewpoint of statistical inference.” (Aris Spanos)

http://errorstatistics.com/2013/12/19/a-spanos-lecture-on-frequentist-hypothesis-testing/

 

Categories: frequentists tests | 1 Comment

Gelman’s error statistical critique of data-dependent selections–they vitiate P-values: an extended comment

The nice thing about having a “rejected posts” blog, which I rarely utilize, is that it enables me to park something too long for a comment, but not polished enough to be “accepted” for the main blog. The thing is, I don’t have time to do more now, but would like to share my meanderings after yesterday’s exchange of comments with Gelman.

I entirely agree with Gelman that in studies with wide latitude for data-dependent choices in analyzing the data, we cannot say the study was stringently probing for the relevant error (erroneous interpretation) or giving its inferred hypothesis a hard time.

One should specify what the relevant error is. If it’s merely inferring some genuine statistical discrepancy from a null, that would differ from inferring a causal claim. Weakest of all would be merely reporting an observed association. I will assume the nulls are like those in the examples in the “The Garden of Forking Paths” paper, only I was using his (2013) version. I think they are all mere reports of observed associations except for the BEM ESP study. (That they make causal, or predictive, claims already discredits them).

They fall into the soothsayer’s trick of, in effect, issuing such vague predictions that they are guaranteed not to fail.

Here’s a link to Gelman and Loken’s “The Garden of Forking Paths” http://www.stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf

I agree entirely: “Once we recognize that analysis is contingent on data, the p-value argument disappears–one can no longer argue that, if nothing were going on, that something as extreme as what was observed would occur less than 5% of the time.” (Gelman 2013, p. 10). The nominal p-values does not reflect the improbability of such an extreme or more extreme result due to random noise or “nothing going on”.

A legitimate p-value of α must be such that

Pr(Test yields P-value < α; Ho: chance) ~ α.

With data dependent hypotheses, the probability the test outputs a small significance level can easily be HIGH, when it’s supposed to be LOW. See this post: “Capitalizing on Chance” reporting on Morrison and Henkel from the 1960s!![i]http://errorstatistics.com/2014/03/03/capitalizing-on-chance-2/

Notice, statistical facts about p-values demonstrate the invalidity of taking these nominal p-values as actual. So statistical facts about p-values are self-correcting or error correcting.

So, just as in my first impression of the “Garden” paper, Gelman’s concern is error statistical: it involves appealing to data that didn’t occur, but might have occurred, in order to evaluate inferences from the data that did occur. There is an appeal to a type of sampling distribution over researcher “degrees of freedom” akin to literal multiple testing, cherry-picking, barn-hunting and so on.

One of Gelman’s suggestions is (or appears to be) to report the nominal p-value, and then consider the prior that would render the p-value = to the resulting posterior. If the prior doesn’t seem believable, I take it you are to replace it with one that does. Then, using whatever prior you have selected, report the posterior probability that the effect is real. (In a later version of the paper, there is only reference to using a “pessimistic prior”.) This is remindful of Greenland’s “dualistic” view. Please search on error statistics.com.

Here are some problems I see with this:

  1. The supposition is that for the p-value to be indicative of evidence for the alternative (say in a one-sided test of a 0 null), the p-value should be like a posterior probability for the null, (1- p) to the non-null. This is questionable. http://errorstatistics.com/2014/07/14/the-p-values-overstate-the-evidence-against-the-null-fallacy/

Aside: Why even allow using the nominal p-value as a kind of likelihood to go into the Bayesian analysis if its illegitimate? Can we assume the probability model used to compute the likelihood from the nominal p-value?

  1. One may select the prior in such a way that one reports a low posterior that the effect is real. There’s wide latitude in the selection and it will depend on the framing of the “not-Ho” (non-null).Now one has “critics degrees of freedom” akin to researchers degrees of freedom.

One is not criticizing the study or pinpointing its flawed data dependencies, yet one is claiming to have grounds to criticize it.

Or suppose the effect inferred is entirely believable and now the original result is blessed—even though it should be criticized as having poorly tested the effect. Adjudicating between different assessments by different scientists will become a matter of defending one’s prior, when it should be a matter of identifying the methodological flaws in the study. The researcher will point to many other “replications” in a big field studying similar effects, etc.

There’s a crucial distinction between a poorly tested claim and an implausible claim. An adequate account of statistical testing needs to distinguish these.

I want to be able to say that the effect is quite plausible given all I know, etc., but this was a terrible test of it, and supplies poor grounds for the reality of the effect.

Gelman’s other suggestion, that these experimenters distinguish exploratory from confirmatory experiments, and that they be required to replicate their results is, on the face of it, more plausible. But the only way this would be convincing, as I see it, is if the data analysts were appropriately blinded. Else, they’ll do the same thing with the replication.

I agree of course that a mere nominal p-value “should not be taken literally” (in the sense that it’s not an actual p-value)—but I deny that this is equal to assigning p as a posterior probability to the null.

There are many other cases in which data-dependent hypotheses are well-tested by the same data used in their construction/selection  Distinguishing cases has been the major goal of much of my general work in philosophy of science (and it carries over into PhilStat).

http://errorstatistics.files.wordpress.com/2013/12/mayo-surprising-facts-article-printed-online.pdf

http://errorstatistics.com/2013/12/15/surprising-facts-about-surprising-facts/

 One last thing: Gelman is concerned that the p-values based on these data-dependent associations are misleading the journals and misrepresenting the results. This may be so in the “experimental” cases. But if the entire field knows that this is a data-dependent search for associations that seem indicative of supporting one or another conjecture, and that the p-value is merely a nominal or computed measure of fit, then it’s not clear there is misinterpretation. It’s just a reported pattern.

[i] When the hypotheses are tested on the same data that suggested them and when tests of significance are based on such data, then a spurious impression of validity may result. The computed level of significance may have almost no relation to the true level. . . . Suppose that twenty sets of differences have been examined, that one difference seems large enough to test and that this difference turns out to be “significant at the 5 percent level.” Does this mean that differences as large as the one tested would occur by chance only 5 percent of the time when the true difference is zero? The answer is no, because the difference tested has been selected from the twenty differences that were examined. The actual level of significance is not 5 percent, but 64 percent! (Selvin 1970, 104)

 

 

Categories: rejected posts | Tags: | 3 Comments

A SANTA LIVED AS A DEVIL AT NASA! Create an easy peasy Palindrome for December, win terrific books for free!

imagesTo avoid boredom, win a free book, and fulfill my birthday request, please ponder coming up with a palindrome for December. If created by anyone younger than 18, they get to select two books. All it needs to have is one word: math (aside from Elba, but we all know able/Elba). Now here’s a tip: consider words with “ight”: fight, light, sight, might. Then just add some words around as needed. (See rules, they cannot be identical to mine.)

Night am…. math gin

fit sight am ….math gist if

sat fight am…math gift as

You can search “palindrome” on my regular blog for past winners, and some on this blog too.

Thanx, Mayo

Categories: palindrome, rejected posts | 6 Comments

More ironies from the replicationistas: Bet on whether you/they will replicate a statistically significant result

For a group of researchers concerned wit how the reward structure can bias results of significance tests, this has to be a joke or massively ironic:

Second Prediction Market Project for the Reproducibility of Psychological Science

The second prediction market project for the reproducibility project will soon be up and running – please participate!

There will be around 25 prediction markets, each representing a particular study that is currently being replicated. Each study (and thus market) can be summarized by a key hypothesis that is being tested, which you will get to bet on.

In each market that you participate, you will bet on a binary outcome: whether the effect in the replication study is in the same direction as the original study, and is statistically significant with a p-value smaller than 0.05.

Everybody is eligible to participate in the prediction markets: it is open to all members of the Open Science Collaboration discussion group – you do not need to be part of a replication for the Reproducibility Project. However, you cannot bet on your own replications.

Each study/market will have a prospectus with all available information so that you can make informed decisions.

The prediction markets are subsidized. All participants will get about $50 on their prediction account to trade with. How much money you make depends on how you bet on different hypotheses (on average participants will earn about $50 on a Mastercard (or the equivalent) gift card that can be used anywhere Mastercard is used).

The prediction markets will open on October 21, 2014 and close on November 4.

If you are willing to participate in the prediction markets, please send an email to Siri Isaksson by October 19 and we will set up an account for you. Before we open up the prediction markets, we will send you a short survey.

The prediction markets are run in collaboration with Consensus Point.

If you have any questions, please do not hesitate to email Siri Isaksson.

Categories: rejected posts | 3 Comments

Msc. kvetch: Are you still fully dressed under your clothes?

UnknownMen have a constitutional right to take pictures under women’s skirts. Yup. That’s what the Massachusetts courts have determine after one Michael Robertson was caught routinely taking pictures and videos up the skirts of women. It even has a name: upskirting.

The Supreme Judicial Court overruled a lower court decision that had upheld charges against Michael Robertson, who was arrested in August 2010 by transit police who set up a sting after getting reports that he was using his cellphone to take photos and video up female riders’ skirts and dresses.

Robertson had argued that it was his constitutional right to do so…..

“A female passenger on a MBTA trolley who is wearing a skirt, dress or the like covering these parts of her body is not a person who is ‘partially nude,’ no matter what is or is not underneath the skirt..”

Link is here.

But this is absurd: she IS partially nude under her clothing, even if she isn’t when you don’t look up her skirt! The picture Robertson took is not of her fully clothed.

People are fully clothed when the TSA conducts whole body scans in airports (a practice that’s largely ended), and yet the pictures would be of the person naked. If you can be partially naked when an instrument sees through your clothes, then you can be partially naked when a cell phone is held under your skirt. Do we really have to get philosophical about these terms…?

Meanwhile, they’re busy trying to pass a law against upskirting in MA. So are guys in Boston  busy getting all the constitutional shots they can in the mean time?

Chris Dearborn, a law professor at Suffolk University in Boston, said the court’s ruling served as a signal to the legislature to act fast, but also likely had Peeping Toms briefly “jumping for joy”. Link is here.

Jumping for joy at violating a woman’s privacy? What kind of Neanderthals are in Boston these days?

 

Seems like upskirting is back in the news, this time in Georgia. Under your skirt is not really a private place, after all. http://time.com/4422772/upskirt-photos-harassment/

Categories: Misc Kvetching | 15 Comments

Msc Kvetch: Is “The Bayesian Kitchen” open for cookbook statistics?

I was sent a link to “The Bayesian Kitchen” http://www.bayesiancook.blogspot.fr/2014/02/blending-p-values-and-posterior.html and while I cannot tell for sure from from the one post, I’m afraid the kitchen might be open for cookbook statistics. It is suggested (in this post) that real science is all about “science wise” error rates (as opposed to it capturing some early exploratory efforts to weed out associations possibly worth following up on, as in genomics). Here were my comments:

False discovery rates are frequentist but they have very little to do with how well warranted a given hypothesis or model is with data. Imagine the particle physicists trying to estimate the relative frequency with which discoveries in science are false, and using that to evaluate the evidence they had for a Standard Model Higgs on July 4, 2012. What number would they use? What reference class? And why would such a relative frequency be the slightest bit relevant to evaluating the evidential warrant for the Higgs particle, nor for estimating its various properties, nor for the further testing that is now ongoing. Instead physicists use sigma levels (and associated p-values)! They show that the probability is .9999999… that they would have discerned the fact that background alone was responsible for generating the pattern of bumps they repeatedly found (in two labs). This is an error probability. It was the basis for inferring that the SM Higgs hypothesis had passed with high severity, and they then moved on to determining what magnitudes had passed with severity. That’s what science is about! Not cookbooks, not mindless screening (which might be fine for early explorations of gene associations, but don’t use that as your model for science in general).

The newly popular attempt to apply false discovery rates to “science wise error rates” is a hybrid fashion that (inadvertently) institutionalizes cookbook statistics: dichotomous “up-down” tests, the highly artificial point against point hypotheses (a null and some alternative of interest—never mind everything else), identifying statistical and substantive hypotheses, and the supposition that alpha and power can be used as a quasi-Bayesian likelihood ratio. And finally, to top it all off, by plucking from thin air the assignments of “priors” to the null and alternative—on the order of .9 and .1—this hybrid animal reports that more than 50% of results in science are false! I talk about this more on my blog errorstatistics.com

(for just one example:

http://errorstatistics.com/2013/11/09/beware-of-questionable-front-page-articles-warning-you-to-beware-of-questionable-front-page-articles-i/)

Categories: Misc Kvetching, Uncategorized | 4 Comments

Msc Kvetch: comment to Kristof at 5a.m.

My comment follows his article

Bridging the Moat Around Universities

By NICHOLAS KRISTOF

My Sunday column is about the unfortunate way America has marginalized university professors–and, perhaps sadder still, the way they have marginalized themselves from public debate. When I was a kid, the Kennedy administration had its “brain trust” of Harvard faculty members, and university professors were often vital public intellectuals who served off and on in government. That’s still true to some degree of economists, but not of most other Ph.D programs. And we’re all the losers for that.

I’ve noticed this particularly with social media. Some professors are terrific on Twitter, but they’re the exceptions. Most have terrific insights that they then proceed to bury in obscure journals or turgid books. And when professors do lead the way in trying to engage the public, their colleagues sometimes regard them with suspicion. Academia has also become inflexible about credentials, disdaining real-world experience. So McGeorge Bundy became professor of government at Harvard and then dean of the faculty (at age 34!) despite having only a B.A.–something that would be impossible today. Indeed, some professors would oppose Bill Clinton getting a tenured professorship in government today because of his lack of a Ph.D, even though he arguably understands government today better than any other American.

In criticizing the drift toward unintelligible academic writing, my column notes that some professors have submitted meaningless articles to academic journals, as experiments, only to see them published. If I’d had more space, I would have gone through the example of Alan Sokal of NYU, who in 1996 published an article in “Social Text” that he described as: “a pastiche of left-wing cant, fawning references, grandiose quotations, and outright nonsense.” Not only was it published, but after the article was unveiled as gibberish, Social Text’s editors said it didn’t much matter: “Its status as parody does not alter, substantially, our interest in the piece, itself, as a symptomatic document.”

I hope people don’t think my column is a denunciation of academia. On the contrary, I think universities are an incredible national resource, with really smart thinking on vital national issues. I want the world to get the benefit of that thinking, not see it hidden in academic cloisters. Your thoughts on this issue?

 

Deborah Mayo Virginia 12 hours ago

In my own field of philosophy, the truth is that the serious work, the work that advances the ideas and research, takes place in “obscure journals or turgid books”. There are plenty of areas where this research can be directly relevant to public issues–it’s the public who should be a bit more prepared to engage with the real scholarship. Take my specialization of philosophy of statistical inference in science. Science writers appear to be only interested in repeating the popular, sexy, alarmist themes (e.g., most research is wrong, statistical significance is bogus,science fails to self-correct). Rather than research what some more careful thinkers have shown, or engage the arguments behind contrasting statistical philosophies–those semi-turgid books–, these science writers call around to obtain superficial dramatic quips from the same cast of characters. They have a one-two recipe for producing apparently radical and popular articles this way. None of the issues ever get clarified this way. I suggest the public move closer to the professional work rather than the other way around. Popular is generally pablam, at least in the U.S.

Categories: Misc Kvetching | Leave a comment

Notes (from Feb 6*) on the irrelevant conjunction problem for Bayesian epistemologists (i)

images-2

 

* refers to our seminar: Phil6334

I’m putting these notes under “rejected posts” awaiting feedback and corrections.

Contemporary Bayesian epistemology in philosophy appeals to formal probability to capture informal notions of “confirmation”, “support”, “evidence”, and the like, but it seems to create problems for itself by not being scrupulous about identifying the probability space, set of events, etc. and not distinguishing between events and statistical hypotheses. There is usually a presumed reference to games of chance, but even there things can be altered greatly depending on the partition of events. Still, we try to keep to that. The goal is just to give a sense of that research program. (Previous posts on the tacking paradox: Oct. 25, 2013: “Bayesian Confirmation Philosophy and the Tacking Paradox (iv)*” &  Oct 25.

(0) Simple Bayes Boost R: 

H is “confirmed” or supported by x if P(H|x) > P(H) (P(x |H)) > P(x).

H is disconfirmed (or undermined) by x if P(H|x) < P(H), (else x is confirmationally irrelevant to H).

Mayo: The error statistician would already get off at (0): probabilistic affirming the consequent is maximally unreliable, violating the minimal requirement for evidence. That could be altered with context-depedent information about how the data and hypotheses are arrived at, but this is not made explicit.

(a) Paradox of irrelevant conjunctions (‘tacking paradox’)

If x confirms H, then x also confirms (H & J), even if hypothesis J is just “tacked on” to H.[1]

Hawthorne and Fitelson (2004) define:

J is an irrelevant conjunct to H, with respect to evidence x just in case

P(x|H) = P(x|H & J).

(b) Example from earlier: For instance, x might be radioastronomic data in support of:

H: “the GTR deflection of light effect is 1.75” and

J: “the radioactivity of the Fukushima water dumped in the Pacific ocean is within acceptable levels”.

(1) Bayesian (Confirmation) Conjunction: If x Bayesian confirms H, then x Bayesian-confirms:

(H & J), where P(xH & J ) = P(x|H) for any J consistent with H

(where J is an irrelevant conjunct to H, with respect to evidence x).

If you accept R, (1) goes through.

Mayo: We got off at (0) already.  Frankly I don’t  know why Bayesian epistemologists would allow adding an arbitrary statement or hypothesis not amongst those used in setting out priors. Maybe it’s assumed J is in there somehow (in the background K), but it seems open-ended, and they have not objected.

But let’s just talk about well-defined events in a probability experiment; and limit ourselves to talking about an event providing evidence of another event (e.g., making it more or less expected) in some sense. In one of Fitelson’s examples,   P(black|ace of spade) > P(black), so “black” confirms it’s an ace of spades (presumably in random drawings of card color from an ordinary deck)–despite “ace” being an “irrelevant conjunct” of sorts. Even so, if someone says data x (it’s a stock trader) is evidence it’s an inside trader in a hedge firm, I think it would be assumed that something had been done to probe the added conjuncts.

(2) Using simple B-boost R: (H & J) gets just as much of a boost by x as does H—measuring confirmation as a simple B-boost: R.

CR(H, x) = CR((HJ), x) for irrelevant conjunct J.

R: P(H|x)/P(H) (or equivalently, P(x |H)/P(x))

(a) They accept (1) but (2) is found counterintuitive (by many or most Bayesian epistemologists). But if you’ve defined confirmation as a B-boost, why run away from the implications? (A point Maher makes.) It seems they implicitly slide into thinking of what many of us want:

some kind of an assessment of how warranted or well-tested H is (with x and background).

(not merely a ratio which, even if we can get it, won’t mean anything in particular. It might be any old thing, 2, 22, even with H scarcely making x expected.).

(b) The intuitive objection according to Crupi and Tentori (2010) is this (e.g., p. 3): “In order to claim the same amount of positive support from x to a more committal theory “H and J” as from x to H alone, …adding J should contribute by raising further how strongly x is expected assuming H by itself. Otherwise, what would be the specific relevance of J?” (using my letters, emphasis added)

But the point is that it’s given that J is irrelevant. Now if one reports all the relevant information for the inference, one might report something like (H & J) makes x just as expected as H alone. Why not object to the “too was” confirmation (H & J) is getting when nothing has been done to probe J? I think the objection is, or should be, that nothing has been done to show J is the case rather than not. P(x |(H & J)) = P(x |(H & ~J)).

(c) Switch from R to LR: What Fitelson (Hawthorne and Fitelson 2004) do is employ, as a measure of the B-boost, what some call the likelihood ratio (LR).

CLR(H, x) = P(x | H)/P(x | ~H).

(3) Let x confirm H, then

(*) CLR(H, x) > CLR((HJ), x)

For J an irrelevant conjunct to H.

So even though x confirms (H & J) it doesn’t get as much as H does, at least if one uses LR. (It does get as much using R).

They see (*) as solving the irrelevant conjunct problem.

(4) Now let x disconfirm Q, and x confirm ~Q, then

(*) CLR(~Q, x) > CLR((~Q & J), x)

For J an irrelevant conjunct to Q: P(x|Q) = P(x|J & Q).

Crupi and Tentori (2010) notice an untoward consequence of using LR confirmation in the case of disconfirmation (substituting their Q for H above): If x disconfirms Q, then (Q & J) isn’t as badly disconfirmed as Q is, for J an irrelevant conjunct to Q. But this just follows from (*), doesn’t it? That is, from (*), we’d get (**) [possibly an equality goes somewhere.)

(**) CLR(Q, x) < CLR((Q & J), x).

This says that if x disconfirms Q, (Q & J) isn’t as badly disconfirmed as Q is. This they find counterintuitive.

But if (**) is counterintuitive, then so is (*).

(5) Why (**) makes sense if you wish to use LR:

The numerators in the LR calculations are the same:

P(x|Q & J) = P(x|Q) and P(x|H & J) = P(x|H) since in both cases J is an irrelevant conjunct.

But P(x|~(Q & J)) < P(x|~Q)

Since x disconfirms Q, x is more probable given ~Q than it is given (~Q v ~J). This explains why

(**) CLR(Q, x) < CLR((Q & J), x)

So if (**) is counterintuitive then so is (*).

(a) Example Qunready for college.

If x = high scores on a battery of college readiness tests, then x disconfirms Q and confirms ~Q.

What should J be? Suppose having one’s favorite number be an even number (rather than an odd number) is found irrelevant to scores.

(i) P(x|~(Q & J)) = P(high scores| either college ready or ~J)

(ii) P(x|~Q ) = P(high scores| college ready)

(ii) might be ~1 (as in the earlier discussion), while (i) considerable less.

The high scores can occur even among those whose favorite number is odd.This explains why

(**) CLR(Q, x) < CLR((Q & J), x)

In the case where x confirms H, it’s reversed

P(x |~(H & J)) > P(x |~H)

(b) Using one of Fitelson’s examples, but for ~Q:

e.g., Q: not-spades    x: black      J: ace

P(x |~Q) = 1.

P(x | Q)= 1/3

P(x |~(Q & J)) = 25/49

i.e., P(black|spade or not ace)=25/49

Note: CLR [(Q & J), x) = P(x |(Q & J))/P(x |~(Q & J))

Please share corrections, questions.

Previous slides are:

http://errorstatistics.com/2014/02/09/phil-6334-day-3-feb-6-2014/

http://errorstatistics.com/2014/01/31/phil-6334-day-2-slides/

REFERENCES:

Chalmers (1999). What Is This Thing Called Science, 3rd ed. Indianapolis; Cambridge: Hacking.

Crupi & Tentori (2010). Irrelevant Conjunction: Statement and Solution of a New Paradox, Phil Sci, 77, 1–13.

Hawthorne & Fitelson (2004). Re-Solving Irrelevant Conjunction with Probabilistic Independence, Phil Sci 71: 505–514.

Maher (2004). Bayesianism and Irrelevant Conjunction, Phil Sci 71: 515–520.

Musgrave (2010) “Critical Rationalism, Explanation, and Severe Tests,” in Error and Inference (D.Mayo & A. Spanos eds). CUP: 88-112.


[1] Chalmers and Musgrave say I should make more of how simply severity solves it, notably for distinguishing which pieces of a larger theory rightfully receive evidence, and a variety of “idle wheels” (Musgrave, p. 110.)

Categories: phil6334 rough drafts | 3 Comments

Winner of the January 2014 Palindrome Contest

images-5Winner of the January 2014 Palindrome Context

Karthik Durvasula
Visiting Assistant Professor in Phonology & Phonetics at Michigan State University

Palindrome: Test’s optimal? Agreed! Able to honor? O no! hot Elba deer gala. MIT-post set.

The requirement was: A palindrome with “optimal” and “Elba”.

BioI’m a Visiting Assistant Professor in Phonology & Phonetics at Michigan State University. My work primarily deals with probing people’s subconscious knowledge of (abstract) sound patterns. Recently, I have been working on auditory illusions that stem from the bias that such subconscious knowledge introduces.

Statement: “Trying to get a palindrome that was at least partially meaningful was fun and challenging. Plus I get an awesome book for my efforts. What more could a guy ask for! I also want to thank Mayo for being excellent about email correspondence, and answering my (sometimes silly) questions tirelessly.”

Book choice: EGEK 1996! 🙂
[i.e.,Mayo (1996): “Error and the Growth of Experimental Knowledge”]

CONGRATULATIONS! And thanks so much for your interest!

February contest: Elba plus deviate (deviation)

Categories: palindrome, rejected posts | 1 Comment

Sir Harold Jeffreys (tail area) howler: Sat night comedy (rejected post Jan 11, 2014)

IMG_0600You might not have thought there could be yet new material for 2014, but there is: for the first time Sir Harold Jeffreys himself is making an appearance, and his joke, I admit, is funny. So, since it’s Saturday night, let’s listen in on Sir Harold’s howler in criticizing p-values. However, even comics try out “new material” with a dry run, say at a neighborhood “open mike night”. So I’m placing it here under rejected posts, knowing maybe 2 or at most 3 people will drop by. I will return with a spiffed up version at my regular gig next Saturday.

Harold Jeffreys: Using p-values implies that “An hypothesis that may be true is rejected because it has failed to predict observable results that have not occurred.” (1939, 316)

I say it’s funny, so to see why I’ll strive to give it a generous interpretation.

We can view p-values in terms of rejecting H0, as in the joke, as follows:There’s a test statistic D such that H0 is rejected if the observed D,i.e., d0 ,reaches or exceeds a cut-off d* where Pr(D > d*; H0) is very small, say .025. Equivalently, in terms of the p-value:
Reject H0 if Pr(D > d0H0) < .025.
The report might be “reject Hat level .025″.

Suppose we’d reject H0: The mean light deflection effect is 0, if we observe a 1.96 standard deviation difference (in one-sided Normal testing), reaching a p-value of .025. Were the observation been further into the rejection region, say 3 or 4 standard deviations, it too would have resulted in rejecting the null, and with an even smaller p-value. H0 “has not predicted” a 2, 3, 4, 5 etc. standard deviation difference. Why? Because differences that large are “far from” or improbable under the null. But wait a minute. What if we’ve only observed a 1 standard deviation difference (p-value = .16)? It is unfair to count it against the null that 1.96, 2, 3, 4 etc. standard deviation differences would have diverged seriously from the null, when we’ve only observed the 1 standard deviation difference. Yet the p-value tells you to compute Pr(D > 1; H0), which includes these more extreme outcomes. This is “a remarkable procedure” indeed! [i]

So much for making out the howler. The only problem is that significance tests do not do this, that is, they do not reject with, say, D = 1 because larger D values, further from might have occurred (but did not). D = 1 does not reach the cut-off, and does not lead to rejecting H0. Moreover, looking at the tail area makes it harder, not easier, to reject the null (although this isn’t the only function of the tail area): since it requires not merely that Pr(D = d0 ; H0 ) be small, but that Pr(D > d0 ; H0 ) be small. And this is well justified because when this probability is not small, you should not regard it as evidence of discrepancy from the null. Before getting to this, a few comments:

1.The joke talks about outcomes the null does not predict–just what we wouldn’t know without an assumed test statistic, but the tail area consideration arises in Fisherian tests in order to determine what outcomes H0 “has not predicted”. That is, it arises to identify a sensible test statistic D (I’ll return to N-P tests in a moment).

In familiar scientific tests, we know the outcomes that are further away from a given hypothesis in the direction of interest, e.g., the more patients show side effects after taking drug Z, the less indicative it is benign, not the other way around. But that’s to assume the equivalent of a test statistic. In Fisher’s set-up, one needs to identify a suitable measure of closeness, fit, or directional departure. Any particular outcome can be very improbable in some respect. Improbability of outcomes (under H0) should not indicate discrepancy from H0 if even less probable outcomes would occur under discrepancies from H0. (Note: To avoid confusion, I always use “discrepancy” to refer to the parameter values used in describing the underlying data generation; values of D are “differences”.)

2. N-P tests and tail areas: Now N-P tests do not consider “tail areas” explicitly, but they fall out of the desiderata of good tests and sensible test statistics. N-P tests were developed to provide the tests that Fisher used with a rationale by making explicit alternatives of interest—even if just in terms of directions of departure.

In order to determine the appropriate test and compare alternative tests “Neyman and I introduced the notions of the class of admissible hypotheses and the power function of a test. The class of admissible alternatives is formally related to the direction of deviations—changes in mean, changes in variability, departure from linear regression, existence of interactions, or what you will.” (Pearson 1955, 207)

Under N-P test criteria, tests should rarely reject a null erroneously, and as discrepancies from the null increase, the probability of signaling discordance from the null should increase. In addition to ensuring Pr(D < d*; H0) is high, one wants Pr(D > d*; H’: μ0 + γ) to increase as γ increases.  Any sensible distance measure D must track discrepancies from H0.  If you’re going to reason, the larger the D value, the worse the fit with H0, then observed differences must occur because of the falsity of H0 (in this connection consider Kadane’s howler).

3. But Fisher, strictly speaking, has only the null distribution, and an implicit interest in tests with sensitivity of a given type. To find out if H0 has or has not predicted observed results, we need a sensible distance measure.

Suppose I take an observed difference d0 as grounds to reject Hon account of its being improbable under H0, when in fact larger differences (larger D values) are more probable under H0. Then, as Fisher rightly notes, the improbability of the observed difference was a poor indication of underlying discrepancy. This fallacy would be revealed by looking at the tail area; whereas it is readily committed, Fisher notes, with accounts that only look at the improbability of the observed outcome d0 under H0.

4. Even if you have a sensible distance measure D (tracking the discrepancy relevant for the inference), and observe D = d, the improbability of d under H0 should not be indicative of a genuine discrepancy, if it’s rather easy to bring about differences even greater than observed, under H0. Equivalently, we want a high probability of inferring H0 when H0 is true. In my terms, considering Pr(D < d*; H0) is what’s needed to block rejecting the null and inferring H’ when you haven’t rejected it with severity. In order to say that we have “sincerely tried”, to use Popper’s expression, to reject H’ when it is false and H0 is correct, we need Pr(D < d*; H0) to be high.

5. Concluding remarks:

The rationale for the tail area is twofold: to get the right direction of departure, but also to ensure Pr(test T does not reject null; H0 ) is high.

If we don’t have a sensible distance measure D, then we don’t know which outcomes we should regard as those H0 does or does not predict. That’s why we look at the tail area associated with D. Neyman and Pearson make alternatives explicit in order to arrive at relevant test statistics. If we have a sensible D, then Jeffreys’ criticism is equally puzzling because considering the tail area does not make it easier to reject H0 but harder. Harder because it’s not enough that the outcome be improbable under the null, outcomes even greater must be improbable under the null. And it makes it a lot harder (leading to blocking a rejection) just when it should: because the data could readily be produced by H0 [ii].

Either way, Jeffreys’ criticism, funny as it is, collapses.

When an observation does lead to rejecting the null, it is because of that outcome—not because of any unobserved outcomes. Considering other possible outcomes that could have arisen is essential for determining (and controlling) the capabilities of the given testing method. In fact, understanding the properties of our testing tool just is to understand what it would do under different outcomes, under different conjectures about what’s producing the data.


[i] Jeffreys’ next sentence, remarkably is: “On the face of it, the evidence might more reasonably be taken as evidence for the hypothesis, not against it.” This further supports my reading, as if we’d reject a fair coin null because it would not predict 100% heads, even though we only observed 51% heads. But the allegation has no relation to significance tests of the Fisherian or N-P varieties.

[ii] One may argue it should be even harder, but that is tantamount to arguing the purported error probabilities are close to the actual ones. Anyway, this is a distinct issue.

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