Frequentist Statistics as a Theory of Inductive Inference (2/27/14)

Frequentist Statistics as a Theory of Inductive Inference (2/27/14)
A conversation I had with Sir David Cox, (“Statistical Science and Philosophy of
Science: Where Do/Should They Meet (in 2011 and beyond)?” began with his
asking:
Cox: Deborah, in some fields foundations do not seem very important, but we both
think that foundations of statistical inference are important; why do you think that is?
Mayo: I think because they ask about fundamental questions about evidence,
inference, and probability. …In statistics we’re so intimately connected to the
scientific interest in learning about the world, we invariably cross into philosophical
questions about empirical knowledge and inductive inference.
When Sir David Cox was unable to make it to give my seminar at the LSE last
December, I presented my construal of his philosophy of “frequentist” statistical
methods (based on scads of papers over 60 years, 2 recent books of his on
“Principles”, our 2 joint papers)

1

 Moving away from caricatures of the standard methods (e.g., significance tests,
confidence intervals) on which critics often focus, the tools emerge as satisfying
piecemeal learning goals within a multiplicity of methods and interconnected
checks, and reports, of error.
 We deny the assumption that the overall unified principle for these methods
must be merely controlling the low-long run error probabilities of methods
(behavioristic rationale),
 We advance an epistemological principle that renders hypothetical error
probabilities relevant for learning about particular phenomena.
 We contrast it with current Bayesian attempts at unifications of frequentist and
Bayesian methods.

2

Decision and Inference: Cox also distinguishes uses of statistical methods for
inference and for decision--- the former is open ended (inferences are not fixed in
advance), without explicit quantification of costs
Background Knowledge: In the statistical analysis of scientific and technological
data, there is virtually always external information that should enter in reaching
conclusions about what the data indicate with respect to the primary question of
interest.
Typically, these background considerations enter not by a probability assignment (as
in the Bayesian paradigm), but by identifying the question to be asked, designing the
study, specifying an appropriate statistical model, interpreting the statistical results,
and relating those inferences to primary scientific ones, and using them to extend and
support underlying theory.
--a very large issue in current foundations

3

Roles of Probability in Induction
Inductive inference: the premises (background and evidence statements) can be true
while the conclusion inferred may be false without a logical contradiction:
the conclusion is "evidence transcending".
probability naturally arises in capturing induction, but there is more than one way this
can occur: To measure:
1. how much belief or support there is for a hypothesis
2. how reliable or how well tested a hypothesis is
 too often presupposed it can only be the former—probabilism leading to wellknown difficulties with using frequentist ideas (universes are not as plenty as
blackberries—Peirce)
 our interest is in how frequentist probability may be used for the latter (for
measuring corroboration, trustworthiness, severity of tests)…

4

For instance, significance testing uses probability to characterize the proportion of
cases in which a null hypothesis H0 would be erroneously rejected in a hypothetical
long-run of sampling, an error probability.
We may even refer to frequentist statistical inference as error probability statistics, or
just error statistics.
Cox’s analogy with calibrating instruments:
"Arguments involving probability only via its (hypothetical) long-run frequency
interpretation are called frequentist. That is, we define procedures for assessing
evidence that are calibrated by how they would perform were they used repeatedly.
In that sense they do not differ from other measuring instrument." (Cox 2006, p. 8)
As with the use of measuring instruments, we employ the performance features to
make inferences about aspects of the particular thing that is measured, (aspects that
the measuring tool is appropriately capable of revealing).

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It’s not hard to see how we do so, if we remember the central problem of inductive
inference
The argument from:
H entails data y, (H “fits” y)
y is observed,
therefore H is correct,
is, of course, deductively invalid.
A central problem is to be able nevertheless to warrant inferring H, or regarding y as
good evidence for H.
Although y accords with H, even in deterministic cases, many rival hypotheses (some
would say infinitely many) would also fit or predict y, and would pass as well as H.

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In order for a test to be probative, one wants the prediction from H to be something
that would be very difficult to achieve, and not easily accounted for, were H false and
rivals to H correct (whatever they may be)
i.e., the test should have high probability of falsifying H, if H is false….

7

Statistical Significance Tests
While taking elements from Fisherian and Neyman-Pearson approaches, our
conception differs from both
1. We have empirical data y treated as observed values of a random variable (vector)
Y.
y is of interest to provide information about the probability distribution of Y as
defined by the relevant statistical model—often an idealized representation of
the underlying data generating process.
2. We have a hypothesis framed in terms of parameters of the distribution, the
hypothesis under test or the null hypothesis H0.
An elementary example:
Data Y consists of n Independent and Identically Distributed (IID) components
(r.v’s), Normally distributed with unknown mean  and possibly unknown standard
deviation  :
Yi ~ NIID(,), i =1,2,…,n.

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 A simple hypothesis is obtained if the value of  is known and H0 asserts that 
= 0, a given constant.
We find a function T = t(Y) of the sample, the test statistic, such that:
(i) The larger the value of t the more inconsistent or discordant the data are with
H0 in the respect tested
(ii) The statistic T = t(Y) has a (numerically) known probability distribution when
H0 is true.
The p-value (observed statistical significance level) corresponding to t(y) is:
P(t(Y) ≥ t(y); H0 ):=P(T ≥ t; H0 )
regarded as a measure of accordance with H0 in the respect tested.
So we have: (1) Data (corresponding sample space) , (2) hypotheses, (3) test statistic,
(4) p-value (or other error probability) as a measure of discordance or accordance
with H0 in the respect tested.

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Low p-values, if properly computed, count as evidence against H0 (evidence of a
specified discrepancy or inconsistency with H0).

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Our treatment contrasts with what is often taken as the strict Neyman-Pearson (N-P)
formulation:
 Rejects recipe-like view of tests, e.g., set a preassigned threshold value  and
"reject" H0
if and only if p ≤  .
However, such “behavioristic” construals have roles in this conception: While a
relatively mechanical use of p-values is widely lampooned, there are contexts where
it serves as a screening device, decreasing the rates of publishing misleading results
(e.g., microarrays)
Virtues: impartiality, relative independence from manipulation, gives protection of
known and desirable long-run properties.
But the main interest and novelty here is developing an inferential or evidential
rationale.
P(t(Y) ≥ t(y); H0 ):=P(T ≥ t; H0 )

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For example: low p-values, if properly computed, count as evidence against H0
(evidence of a specified discrepancy or inconsistency with H0).
Why?

12

It’s true that such a rule provides low error rates (i.e., erroneous rejections) in the
long run when H0 is true, a behavioristic argument:
Suppose that we were to treat the data as just decisive evidence against H0, then, in
hypothetical repetitions, H0 would be rejected in a long-run proportion p of the
cases in which it is actually true.
(so a p-value is an error probability unlike what many allege)
But what matters for us, is that such a rule also provides a way to determine whether
a specific data set provides evidence of inconsistency with or discrepancy from H0.
…along the lines of the probative demand for tests…

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FEV: Frequentist Inductive Inference Principle
The reasoning is based on the following frequentist inference principle (with respect
to H0):
(FEV) y fails to count as evidence against H0,
if, such discordant results are fairly frequent even if H0 is correct. Actually FEV(i)
(Mayo and Cox 2006/2010, 254)1
y counts as evidence of a discrepancy only if (and to the extent that) a less discordant
result would probably have occurred, if H0 correctly described the distribution
generating y.
So if the p-value is not small, it is fairly easy (frequent) to generate such discordant
results even if the null is true, so this is not good evidence of a discrepancy from the
null…
Where “such discordant results” are those as or even further from H0 than the
outcome observed.

14

Weight Gain Example.
To distinguish between this "evidential" use of significance test reasoning, and the
familiar appeal to “low long-run erroneous behavior” (N-P), consider a very informal
example:
Suppose that weight gain is measured by a multiplicity of well-calibrated and stable
methods, and the results show negligible change over a test period (e.g., before and
after England).
This is grounds for inferring that my weight gain is negligible within limits set by the
sensitivity of the scales.
Why?
While it is true that by following such a procedure in the long run one would rarely
report weight gains erroneously, that is not the rationale for the particular inference.

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The justification is rather that the error probabilistic properties of the weighing
procedure reflect what is actually the case in the specific instance.
(It informs about the specific cause of the lack of a recorded increase in weight).
Low long run error, while necessary, is not sufficient for warranted evidence in a
particular case.
General Severity Account of Evidence for statistical and non-statistical cases:
FEV falls under a more general account of evidence or inductive inference, extending
beyond statistical hypotheses:
Data y provide evidence for a claim H, just to the extent that H passes a severe test
with y

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The intuition behind requiring severity is that:
Data y0 in test T provide good evidence for inferring H (just) to the extent that H
passes severely with y0, i.e., to the extent that H would (very probably) not have
survived the test so well were H false.
This may be a quantitative or a qualitative assessment.
(A core question that permeates "inductive philosophy", both in statistics and
philosophy is:
What is the nature and role of probabilistic concepts, methods, and models in making
inferences in the face of limited data, uncertainty and error?

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Mayo and Cox (2010) identify a key principle —
Frequentist Evidential Principle (FEV):
(FEV) — by which hypothetical error probabilities may be used for inductive
inference from specific data and consider how FEV may direct and justify:
(a) Different uses and interpretations of statistical significance levels in testing a
variety of different types of null hypotheses,
and
(b) when and why "selection effects" need to be taken account of in data dependent
statistical testing.

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The Role of Probability in Frequentist Induction
The defining feature of an inductive inference is that the premises (background
and evidence statements) can be true while the conclusion inferred may be false
without a logical contradiction:
the conclusion is "evidence transcending".
Probability naturally arises but there is more than one way this can occur.
Two distinct philosophical traditions for using probability in inference are summed
up by Pearson (1950, p. 228) (probabilism and performance):
“For one school, the degree of confidence in a proposition, a quantity varying with
the nature and extent of the evidence, provides the basic notion to which the
numerical scale should be adjusted.
The other school notes the relevance in ordinary life of a knowledge of the relative
frequency of occurrence of a particular class of events in a series of repetitions, and
suggests that "it is through its link with relative frequency that probability has the
most direct meaning for the human mind"

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Frequentist induction, employs probability in the second manner:
For instance, significance testing appeals to probability to characterize the proportion
of cases in which a null hypothesis H0 would be rejected in a hypothetical long-run of
repeated sampling, an error probability.
We may even refer to frequentist statistical inference as error probability statistics, or
just error statistics.
Some, following Neyman, describe the role of probability in frequentist error
statistics as “inductive behavior”:
 Here the inductive reasoner "decides to infer" the conclusion, and probability
quantifies the associated risk of error.
The idea that one role of probability arises in science to characterize the "riskiness"
or probativeness or severity of the tests to which hypotheses are put is reminiscent of
the philosophy of Karl Popper.

20

Popper and Neyman:
 Popper and Neyman have a broadly analogous approach based on the idea that
we can speak of a hypothesis having been well-tested in some sense, quite
different from its being accorded a degree of probability, belief or confirmation;
 Both also broadly shared the view that in order for data to "confirm" or
"corroborate" a hypothesis H, that hypothesis would have to have been subjected
to a test with high probability or power to have rejected it if false.
But the similarities between Neyman and Popper do not go as deeply as one might
suppose.
 Despite the close connection of the ideas, there appears to be no reference to
Popper in the writings of Neyman (Lehmann, 1995, p. 3) and the references by
Popper to Neyman are scarce (e.g.,LSD).
 Moreover, because Popper denied that any inductive claims were justifiable, his
philosophy forced him to deny that even the method he espoused (conjecture
and refutations) was reliable (not that it’s known to be unreliable)

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 Although H might be true, Popper made it clear that he regarded corroboration
at most as a report of the past performance of H: it warranted no claims about
its reliability in future applications.

22

Popper and Fisher:
Popper’s focus on falsification by Popper as the goal of tests, and indeed, science, is
clearly strongly redolent of Fisher.
While, again, evidence of direct influence is virtually absent, Popper would have
concurred with Fisher (1935a, p. 16) that ‘every experiment may be said to exist only
in order to give the facts the chance of disproving the null hypothesis’.
However, Popper's position denies ever having grounds for inference about
reliability, or for inferring reproducible deviations.
 By contrast, a central feature of frequentist statistics is to actually assess and
control the probability that a test would have rejected a hypothesis, if false.
The advantage in the modern statistical framework is that the probabilities arise from
defining a probability (statistical) model to represent the phenomenon of interest.
Neyman describes frequentist statistics as modelling the phenomenon of the stability
of relative frequencies of results of repeated "trials", granting there are other

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possibilities concerned with modelling psychological phenomena connected with
intensities of belief, or with readiness to bet specified sums, etc.
Frequentist statistical methods can (and often do) supply tools for inductive
inference by providing methods for evaluating the severity or probativeness of tests
—although they don’t directly do so, the severity rationale gives guidance in using
them in this way.

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A hypothesis H passes a severe test T with data y0 if,
(S-1) y0 agrees with H, and
(S-2) with very high probability, test T would have produced a result that
accords less well with H than y0 does, if H were false.
In statistical contexts, the test statistic t(Y) must define an appropriate measure of
accordance or distance (as required by severity condition (S-1).
Note: when t(Y) is statistically significantly different from H0, it is agreeing with notH0
‘The severity with which inference H passes test T with outcome y’ may be
abbreviated by:
SEV(Test T, outcome y, claim H)

25

Just as one makes inferences about changes in body mass based on performance
characteristics of various scales, error probabilities of tests indicate the capacity
of the particular test to have revealed inconsistencies in the respects probed, and
this in turn allows relating p-values to statements about the underlying process.
Peirce: shall we assume instead the scales read my mind and mislead me just when I
don't know the weight, but doo fine with items of known weight, e.g., 5 lb potatoes.
—That itself a highly unreliable way to proceed in reasoning from instruments…
Note: Clue to showing counterinduction is unreliable.

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A Multiplicity of Types of Null Hypotheses (Cox’s taxonomy)
Considerable criticisms and abuses of significance tests would have been avoided had
Cox’s taxonomy in (1958) been an integral part of significance testing.
Types of Null Hypothesis (multiple uses in piecemeal steps involved in
linking data and models to learn about modeled phenomena)
1. Embedded null hypotheses
2. Dividing null hypotheses:
3. Null hypotheses of absence of structure
4. Null hypotheses of model adequacy
5. Substantively-based null hypotheses.
Evaluating such local hypotheses for these piecemeal learning goals demands its
own criteria

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1. Embedded null hypotheses
Consider a parametric family of distributions f(y;  indexed by unknown (vector of)
parameters = 
where  denotes unknown nuisance parameter(s).
The null and alternative hypotheses of interest are:
H0: vs. H1: 
Central tasks, often ignored, take central stage in Cox’s discussion and indeed are an
important part of the justification of this approach (though must be skimpy here):
(a) —need to choose appropriate test statistic T(Y)
(b) —need to be able to compute the probability distribution of T under the null (and
perhaps alternative) hypotheses (it should not depend on nuisance parameters)

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(c) —need to collect data y to so that they satisfy adequately the assumptions of the
relevant probability model.
If we succeed with all that….
p-value not small—evidence the data are consistent with the null…
But no evidence against is not automatically evidence for the null…
but we can infer the absence of a discrepancy from (or concordance with) H0
To infer the absence of a discrepancy from H0 as large as  examine the probability

We have a result and it differs from H0 by some observed amount d.
 = Prob[a result that differs as much as or more from H0 (than d); evaluated under
the assumption that ],
 = P(t(Y) > t(y) ; 
If is near 1, then, following FEV, the data are good evidence that 
Terms: discrepancy refers to parameters difference, discord, fit refers to observed
differences.


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Interpreting 

 may be regarded as stringency or severity with which the test has probed the
discrepancy ;
that is, has passed a severe test:
(other terms: precision, sensitivity)
 Avoids unwarranted interpretations of “consistency with H0” with insensitive
tests (“fallacies of acceptance”).
 A fallacy of acceptance takes an insignificant difference as evidence that the null
is true; but tests have limited discernment power.
 More relevant to specific data than is a test’s power, which is calculated relative
to a predesignated critical value c beyond which the test "rejects" the null.
Power at 

() = P(t(Y) > c ; 

In contrast:

 = P(t(Y) > t(y) ; 

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An important passage in Cox 2008, p. 25)
“In the Neyman-Pearson theory of tests, sensitivity of a test is assessed by the notion
of power….. In the approach adopted here the assessment is via the distribution of
the random variable P, again considered for various alternatives”
P here is the significance probability regarded as a random variable—which it is…
Cox makes an equivalent move referring to confidence intervals—I'll come back to…
small p-values (evidence of some discrepancy in direction of alternative).
Critics correctly note that the p-value alone doesn’t tell you the size of the
discrepancy indicated “effect size”), but it, together with sample size, can be used to
determine this:
 If a test is so sensitive that so small a p-value is probable, even when ,
then a small p-value is not evidence of a discrepancy from H0 in excess of .
So by this reasoning we also avoid “fallacies of rejection”

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5. Substantively-based null hypotheses.
A theory T predicts that H0 is at least a very close approximation to true situation
(perhaps T has already passed several theoretical and empirical tests)
Rival theory T* predicts a specified discrepancy from H0 and the test is designed to
discriminate between T and T* in a thus far untested domain.
Focus on interpreting: p-value not small.
H0 may be formed deliberately to let T “stick its neck out”
Discrepancies from T in the direction of T* are given a very good chance to be
detected, so if no significant departure is found, this constitutes evidence for T in the
respect tested
Famous “null results” take this form (e.g., set the GTR predicted values at 0)
rivals to the GTR predicted a breakdown of the Weak Equivalence Principle (WEP)
for massive self-gravitating bodies, the earth-moon system: this effect, the Nordvedt
effect would be 0 for GTR (identified with the null hypothesis) and non-0 for rivals.

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Measurements of the round trip travel times between the earth and moon (between
1969 and 1975) set upper bounds to the possible violation of the WEP, and because
the tests were sufficiently sensitive, these measurements provided good evidence that
the Nordvedt effect is absent, i.e., evidence for the null hypothesis
Such a negative result does not provide evidence for all of GTR (in all its areas of
prediction), but it does provide evidence for its correctness with respect to this effect.
Some argue I should allow inferring all of GTR (Chalmers, Laudan, Musgrave) —but
I think it is this piecemeal way that inquiries enable progress (realizing that NOT all
of GTR has been warranted provoked developing rivals…)
Unlike the large-scale theory testers, we are not after an account of “theory choice”
but rather learning about aspects of theoretical phenomena by local probes…
Statistical reasoning is at the heart of inquiries which are broken down into questions
about parameters in statistical distributions intermediate between the full theory and
the actual data.
My conception is to view them as probing for piecemeal errors—and note, an error
can be any mistaken claim or flawed understanding—both empirical and theoretical!

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 A fundamental tenet of the conception of inductive learning most at home with
the frequentist philosophy is that inductive inference requires building up
incisive arguments and inferences by putting together several different piecemeal results.
 Although the complexity of the story makes it more difficult to set out neatly, as,
for example, if a single algorithm is the whole of inductive inference, the payoff
is an account that approaches the kind of full-bodied arguments that scientists
build up in order to obtain reliable knowledge.
 The goal is not representing beliefs or opinions (as in personalist Bayesian
accounts) but avoiding being misled by beliefs and opinions

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Series of Confidence Intervals (goal: to distinguish inferential and
behaviorist justifications while avoiding an infamous fallacy of R.A. Fisher)
Consider our Normal mean “embedded” example, rather than running a
significance test we may form the
1 − α upper confidence bound, CIU(Y; α)
for estimating mean µ, let  be known, y= n-.5.
The upper 1 - α limit is Y + k(α)y
k(α) the upper α-value of the standard Normal distribution. Pick a small α, say .025
The upper .975 limit is Y + 1.96y
Consider the inference:
Infer (or regard the data as evidence for)
µ  y + 1.96y
CIU(Y; .025)

35

One rationale is that it instantiates an inference rule that yields true claims with high
probability (.975) because
P(µ  Y + 1.96y)  .975
Whatever the true value of µ

36

The procedure has high long-run “coverage probabilities.”
They "rub-off" on the particular case.
Instead we might view µ  y + 1.96syas an inference from a type of reductio ad
absurdum argument:
suppose in fact that this inference is false and the true mean is µ*, where µ*
y + 1.96y.
Then it is very probable that we would have observed a larger sample mean:
P( Y  y ; µ*)  .975.
Therefore, one can reason, y is inconsistent at level .975 (or .025) with having been
generated from a population with µ in excess of the upper limit.
This reasoning is captured in FEV
-------------------------------------------------

37

Aside: This statistic is directly related to a test of µ  µ0 against µ  µ0.
In particular, Y is statistically significantly smaller than values of µ in excess of
CIU(Y; α) at level α.

38

Fisher's fiducial error
This may make  look like a random variable----but it is not; these claims do not
hold once a specific y is plugged in for Y
P ( < Y  0x) = .5
P ( < Y  .5x) = .7
P ( < Y  1x) = .84
P ( < Y  1.5x) = .93
P ( < Y  1.96 x) = .975
“Our (Cox and Hinkley) attitude toward the statement [ < Y  1.96 x] might then
be taken to be the same as that to other uncertain statements for which the probability
is [.975] of their being true, and hence…is virtually indistinguishable from a
probability statement about []. However, this attitude is in general incorrect, in our
view because the confidence statement can be known to be true or untrue…… The
system of confidence limits simply summarizes what the data tell us about , given
the model….It is wrong to combine confidence limit statements about different
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parameters as though the parameters were random variables.” (Cox and Hinkley
1974, p. 227)
But what exactly is it telling us about 

40


P ( < Y  0x) = .5
P ( < Y  .5x) = .7
P ( < Y  1x) = .84
P ( < Y  1.5x) = .93
P ( < Y  1.96 x) = .975
Answer (Mayo): it tells you which discrepancies are and are not indicated (at various
degrees of severity)
If we replace P with SEV (the degree of severity or even “corroboration”) the claims
are true even after substituting the observed sample mean
(the severity with which the inference has passed the test with the data)

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SEV( < Y 0  x) to abbreviate:
The severity with which a test T with an observed result Y 0 passes:
( < Y 0  x).
SEV ( < Y 0  0x) = .5
SEV ( < Y 0  .5x) = .7
SEV ( < Y 0  1x) = .84
SEV ( < Y 0  1.5x) = .93
SEV ( < Y 0  1.96 x) = .975
But aren’t I just using this as another way to say how probable each claim is?
No. This would lead to inconsistencies (if we mean mathematical probability), but
the main thing is, or so I argue, probability gives the wrong logic for “how welltested” (or “corroborated”) a claim is

42

What Would a Logic of Severe Testing be?
If H has passed a severe test T with x, there is no problem in saying x warrants H, or,
if one likes, x warrants believing in H….
(H could be the confidence interval estimate)
If SEV(H ) is high, its denial is low, i.e., SEV(~H ) is low
But it does not follow a severity assessment should obey the probability calculus, or
be a posterior probability….
For example, if SEV(H ) is low, SEV(~H ) could be high or low
……in conflict with a probability assignment
(there may be a confusion of ordinary language use of “probability”)

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Other points lead me to deny probability yields a logic we want for well-testedness2
(e.g., problem of irrelevant conjunctions, tacking paradox for Bayesians and
hypothetico-deductivists)
This is different from what I call the
Contrast again with the rubbing off construal: The procedure is rarely wrong,
therefore, the probability it is wrong in this case is low.
The long-run reliability of the rule is a necessary but not a sufficient condition to
infer H (with severity)
The reasoning instead is counterfactual:
H:

 < Y 0  1.96x

(i.e.,  < CIu )

passes severely because were this inference false, and the true mean  > CIu
then, very probably, we would have observed a larger sample mean:

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A very well known criticism (many Bayesians say it is a reductio) of frequentist
confidence intervals stems from assuming that confidence levels must give degrees
of belief, critics mount
e.g., a 95% confidence interval might be known to be correct, (given some additional
constraints about the parameter)--trivial intervals
In our construal, this is just saying no parameter values are ruled out with severity…..
“Viewed as a single statement [the trivial interval] is trivially true, but, on the other hand,
viewed as a statement that all parameter values are consistent with the data at a particular
level is a strong statement about the limitations of the data.” (Cox and Hinkley1974, p.
226)3

45

Mayo and Spanos: Severity Interpretations for T+ of “accept” and “reject”

(SEV) for non-statistically-significant results
For “Accept” H0 (a statistically insignificant difference from 0)
 ≤ Y 0 + k( √n) passes with severity (1 - ,
where P(d(X) > k) =  (for any 0 <  < 1).

(SEV) for statistically-significant results
For Reject H0 (a statistically significant difference from 0)
 > Y 0  k( √n) passes with severity (1 - 

46

FEV(ii): A moderate p-value is evidence of the absence of a discrepancy  from H0
only if there is a high probability the test would have given a worse fit with H0 (i.e.,
smaller p-value) were a discrepancy  to exist
1

47

Frequentist Statistics as a Theory of Inductive Inference (2/27/14)

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Frequentist Statistics as a Theory of Inductive Inference (2/27/14)