The cult of statistical significance: how the standard error costs us jobs, justice, and lives
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Ann Arbor
Univ. of Michigan Press
2008
|
| Schriftenreihe: | Economics, cognition, and society series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Literaturverz. S. 265 - 287 |
| Beschreibung: | XXIII, 321 S. Ill., graph. Darst. |
| ISBN: | 9780472070077 9780472050079 047207007X 0472050079 |
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| adam_text | Contents
Preface
xv
Acknowledgments
xix
A Significant Problem
ι
In many of the life and human sciences the existence/whether question of the philosophical
disciplines has substituted for the size-matters/how-much question of the scientific disci¬
plines. The substitution is causing a loss of jobs, justice, profits, environmental quality, and
even life. The substitution we are worrying about here is called statistical significance
—
a qualitative, philosophical rule that has substituted for a quantitative, scientific magnitude
and judgment.
1. Dieting Significance and the Case of Vioxx
23
Since R. A. Fisher (i
890-1962)
the sciences that have put statistical significance at their cen¬
ters have misused it. They have lost interest in estimating and testing for the actual effects
of drugs or fertilizers or economic policies. The big problem began when Fisher ignored the
size-matters/how-much question central to a statistical test invented by William Sealy
Gös¬
set
(1876-1937), so-called Student s t. Fisher substituted for it a qualitative question con¬
cerning the existence of an effect, by which he meant low sampling error by an arbi¬
trary standard of variance. Forgetting after Fisher what is known in statistics as a minimax
strategy, or other loss function, many sciences have fallen into a sizeless stare. They seek
sampling precision only. And they end by asserting that sampling precision just is oomph,
magnitude, practical significance. The minke and sperm whales of Antarctica and the users
and makers of Vioxx are some of the recent victims of this bizarre ritual.
2.
The Sizeless Stare of Statistical Significance
33
Crossing frantically a busy street to save your child from certain death is a good gamble.
Crossing frantically to get another mustard packet for your hot dog is not. The size of the
potential loss if you don t hurry to save your child is larger, most will agree, than the po¬
tential loss if you don t get the mustard. But a majority of scientists in economics, medicine,
and other statistical fields appear not to grasp the difference. If they have been trained in ex¬
clusively Fisherian methods (and nearly ail of them have) they look only for a probability
of success in the crossing
—
the existence of a probability of success better than
.99
or
.95
or
.90,
and this within the restricted frame of sampling
—
ignoring in any spiritual or financial
currency the value of the prize and the expected cost of pursuing it. In the life and human
sciences a majority of scientists look at the world with what we have dubbed the sizeless
stare of statistical significance.
X
-г*
Contents
3.
What the Sizeless Scientists Say in Defense
42
The sizeless scientists act as if they believe the size of an effect does not matter. In their hearts
they do care about size, magnitude, oomph. But strangely they don t measure it. They sub¬
stitute significance measured in Fisher s way. Then they take the substitution a step further
by limiting their concern for error to errors in sampling only. And then they take it a step fur¬
ther still, reducing all errors in sampling to one kind of error
—
that of excessive skepticism,
Type I error. Their main line of defense for this surprising and unscientific procedure is
that, after all, statistical significance, which they have calculated, is objective. But so too
are the digits in the New York City telephone directory, objective, and the spins of a roulette
wheel. These are no more relevant to the task of finding out the sizes and properties of viruses
or star clusters or investment rates of return than is statistical significance. In short, statisti¬
cal scientists after Fisher neither test nor estimate, really, truly. They testimate.
4.
Better Practice:
β-
Importance vs. «- Significance
57
The most popular test was invented, we ve noted, by
Gösset,
better known by his pen name
Student, a chemist and brewer at Guinness in Dublin.
Gösset
didn t think his test was
very important to his main goal, which was of course brewing a good beer at a good price.
The test,
Gösset
warned right from the beginning, does not deal with substantive impor¬
tance. It does not begin to measure what
Gösset
called real error and pecuniary advan¬
tage, two terms worth reviving in current statistical practice. But Karl Pearson and espe¬
cially the amazing Ronald Fisher didn t listen. In two great books written and revised during
the
işzos
and
1930s,
Fisher imposed a Rule of Two: if a result departs from an assumed hy¬
pothesis by two or more standard deviations of its own sampling variation, regardless of the
size of the prize and the expected cost of going for it, then it is to be called a significant
scientific finding. If not, not. Fisher told the subjectivity-phobic scientists that if they wanted
to raise their studies to the rank of sciences* they must employ his rule. He later urged
them to ignore the size-matters/how-much approaches of
Gösset, Neyman, Egon
Pearson,
Wald,
Jeffreys, Deming, Shewhart, and Savage. Most statistical scientists listened to Fisher.
5.
A Lot Can Go Wrong in the Use of Significance Tests
in Economics
62
We ourselves in our home field of economics were long enchanted by Fisherian significance
and the Rule of Two. But at length we came to wonder why the correlation of prices at
home with prices abroad must be within two standard deviations of
1.0
in the sample be¬
fore one could speak about the integration of world markets. And we came to think it
strange that the U.S. Department of Labor refused to discuss black teenage unemployment
rates of
30
or
40
percent because they were, by Fisher s circumscribed definition, insignifi¬
cant. After being told repeatedly, if implausibly, that such mistakes in the use of Gosset s
test were not common in economics, we developed in the
1990s
a questionnaire to test in
economics articles for economic as against statistical significance. We applied it to the be¬
havior of our tribe during the
1980s.
6.
A Lot Did Go Wrong in the American Economic Review
during the
1980s 74
We did not study the scientific writings of amateurs. On the contrary, we studied the Amer¬
ican Economic Review (known to its friends as the
AER),
a leading journal of economics.
With questionnaire in hand we read every full-length article it published that used a test of
statistical significance from January
1980
to December
1989.
As we expected, in the
1980s
more than
70
percent of the articles made the significant mistake of R. A. Fisher.
Contents ■**■ xi
7.
Is Economic Practice Improving?
79
We published our article in
1996.
Some of our colleagues replied, In the old days [of the
1980s]
people made that mistake, but [in the
Г990Ѕ]
we modern sophisticates do not. So
in Z004 we published a follow-up study, reading all the articles published in the
AER
in the
next decade, the
1990s.
Sadly, our colleagues were again mistaken. Since the
1980s
the prac¬
tice in important respects got worse, not better. About
80
percent of the articles made the
mistaken Fisherian substitution, failing to examine the magnitudes of their results. And less
than
10
percent showed full concern for oomph. In a leading journal of economics, in other
words, nine out of ten articles in the
1990s
acted as if size doesn t matter lor deciding
whether a number is big or small, whether an effect is big or small enough to matter. The
significance asterisk, the flickering star of
*,
has become a totem of economic belief.
8.
How Big Is Big in Economics?
89
Does globalization hurt the poor, does the minimum wage increase unemployment, does
world money cause inflation, does public welfare undermine self-reliance? Such scientific
questions are always matters of economic significance. How much hurt, increase, cause, un¬
dermining? Size matters. Oomph is what we seek. But that is not what is found by the sta¬
tistical methods of modern economics.
9.
What the Sizeless Stare Costs, Economically Speaking
98
Sizeless economic research has produced mistaken findings about purchasing power parity,
unemployment programs, monetary policy, rational addiction, and the minimum wage. In
truth, it has vitiated most econometric findings since the
1920s
and virtually all of them
since the significance error was institutionalized in the
1940s.
The conclusions of Fisherian
studies might occasionally be correct. But only by accident.
10.
How Economics Stays That Way: The Textbooks
and the Referees
106
New assistant professors are not to blame. Look rather at the report card of their teachers
and editors and referees
—
notwithstanding cries of anguish from the wise Savages,
Zellners,
Grangers, and Learners of the economics profession. Economists received a quiet warning
by
F. Y.
Edgeworth in
1885—
too quiet, it seems
—
that sampling precision is not the same as
oomph. They ignored it and have ignored other warnings, too.
11.
The Not-Boring Rise of Significance in Psychology
123
Did other fields, such as psychology, do the same? Yes. In 1919 Edwin Boring warned his
fellow psychologists about confusing so-called statistical with actual significance. Boring
was a famous experimentalist at Harvard. But during his lectures on scientific inference his
colleagues appear to have dozed off. Fisher s
5
percent philosophy was eventually codified
by the Publication Manual of the American Psychological Association, which dictated the
erroneous method worldwide to thousands of academic journals in psychology, education,
and related sciences, including forensics.
12.
Psychometrics Lacks Power
131
Power is a neglected statistical offset to the first kind of error of null-hypothesis signifi¬
cance testing. Power assigns a likelihood to the second kind of error, that of undue gulli¬
bility. The leading journals of psychometrics have had their power examined by insiders to
the field. The power of most psychological science in the age of Fisher turns out to have been
xii ■*■ Contents
embarrassingly low or, in more than a few cases, spuriously high
—
as was found in a sev¬
enty-thousand-observation examination of the matter. Like economists the psychologists de¬
veloped a fetish for testimation and wandered away from powerful measures of oomph.
13.
The Psychology of Psychological Significance Testing
140
Psychologists and economists have said for decades that people are Bayesian learners or
Neyman-Pearson signal detectors. We learn by doing and staying alert to the signals. But
when psychologists and others propose to test those very hypotheses they use Fisher s Rule
of Two. That is, they erase their own learning and power to detect the signal. They seek a
foundation in a Popperian falsificationism long known to be philosophically dubious. What
in logic is called the fallacy of the transposed conditional has grossly misled psychology
and other sizeless sciences. An example is the overdiagnosis of schizophrenia.
14.
Medicine Seeks a Magic Pill
j
54
We found that medicine and epidemiology, too, are doing damage with Student s
í
—
more
in human terms perhaps than are economics and psychology. The scale along which one
would measure oomph is very clear in medicine: life or death. Cardiovascular epidemiology,
to take one example, combines with gusto the fallacy of the transposed conditional and the
sizeless stare of statistical significance. Your mother, with her weak heart, needs to know the
oomph of a treatment. Medical testimators aren t saying.
15.
Rothman s Revolt
165
Some medical editors have battled against the
5
percent philosophy. But even the New En¬
gland Journal of Medicine could not lead medical research back to William Sealy
Gösset
and the promised land of real science. Neither could the International Committee of Med¬
ical Journal Editors, though covering worldwide hundreds of journals. Kenneth Rothman,
the founder of Epidemiology, forced change in his journal. But only his journal. Decades ago
a sensible few in education, ecology, and sociology initiated a significance test controversy.
But grantors, journal referees, and tenure committees in the statistical sciences had faith that
probability spaces can judge
—
the judgment merely that
p
< .05
is better for variable
X than
p
< .11
for variable Y. It s not. It depends on the oomph of X and Y.
16.
On Drugs, Disability, and Death
176
The upshot is that because of Fisher s standard error you are being given dangerous medi¬
cines, and are being denied the best medicines. The Centers for Disease Control is infected
with p-values in a grant, for example, to study drug use in Atlanta. Public health has been
infected, too. An outbreak of salmonella in South Carolina was studied using significance
tests. In consequence a good deal of the outbreak was ignored. In
1995
a Cancer Trialists
Collaborative Group came to a rare consensus on effect size: ten different studies agreed
that a certain drug for treating prostate cancer can increase patient survival by
іг
percent.
An eleventh study published in the New England Journal of Medicine dismissed the drug.
The dismissal was based not on effect size bounded by confidence intervals based on what
Gösset
called real error but on a single p-valae only, indicating, the Fisherian authors be¬
lieved, no clinically meaningful improvement in survival.
17.
Edgeworth s Significance
187
The history of this persistent but mistaken practice is a social study of science. In
1885
an
eccentric and brilliant Oxford don, Francis Ysidro Edgeworth, coined the very term signifi¬
cance. Edgeworth was prolific in science and philosophy, but was especially interested in
Contents r* xiii
watching bees and wasps. In measuring their behavioral differences, though, he focused on
the sizes and meanings of the differences. He never depended on statistical significance.
18.
Take
3σ
as Definitely Significant : Pearson s Rule
193
By contrast, Edgeworth s younger colleague in London, the great and powerful Karl Pear¬
son, used significance very heavily indeed. As such things were defined in
1900
Pearson
was an advanced thinker
—
for example, he was an imperialist and a racist and one of the
founding fathers of neopositivism and eugenics. Seeking to resolve a tension between pas¬
sion and science, ethics and rationality, Pearson mistook significance for revelations about
the objective world. In
1901
he believed
1.5
to
3
standard deviations were definitely sig¬
nificant. By
1906,
he tried to codify the sizeless stare with a Rule of Three and tried to
teach it to
Gösset.
19.
Who Sits on the Egg of Cuadus Canorus?
Not Karl Pearson
203
Pearson s journal, Biometrika (1901-
),
was for decades a major nest for the significance mis¬
take. An article on the brooding habits of the cuckoo bird, published in the inaugural vol¬
ume, shows the sizeless stare at its beginnings.
20. Gösset:
The Fable of the Bee
207
Gösset
revolutionized statistics in
1908
with two articles published in this same Pearson s
journal, The Probable Error of a Mean and The Probable Error of a Correlation Co¬
efficient.
Gösset also
independently invented Monte Carlo analysis and the economic de¬
sign of experiments. He conceived in
1926
the ideas if not the words of power and loss,
which he gave to
Egon
Pearson and
Jerzy Neyman
to complete. Yet most statistical work¬
ers know nothing about
Gösset.
He was exceptionally humble, kindly to other scientists, a
good father and husband, altogether a paragon. As suits an amiable worker bee, he planted
edible berries, blew a pennywhistle, repaired entire, functioning fishing boats with a
penknife, and
—
though a great scientist
—
was for thirty-eight years a businessman brewing
Guinness.
Gösset
always wanted to answer the how-much question. Guinness needed to
know. Karl Pearson couldn t understand.
21.
Fisher: The Fable of the Wasp
214
The tragedy in the fable arose from
Gösset
the bee losing out to R. A. Fisher the wasp. All
agree that Fisher was a genius. Richard Dawkins calls him the greatest of Darwin s succes¬
sors. But Fisher was a genius at a certain kind of academic rhetoric and politics as much as
at mathematical statistics and genetics. His ascent came at a cost to science
—
and to
Gösset.
22.
How the Wasp Stung the Bee and Took
over Some Sciences
227
Fisher asked
Gösset
to calculate Gosser s tables of
t
for him, gratis. He then took Gosset s
tables, copyrighted them for himself, and in the journal
Metron
and in his Statistical Meth¬
ods for Research Workers, later to be published in thirteen editions and many languages, he
promoted his own circumscribed version of Gosset s test. The new assignment of authorship
and the faux machinery for science were spread by disciples and by Fisher himself to Amer¬
ica and beyond. For decades Harold Hotelling, an important statistician and economist, en¬
thusiastically carried the Fisherian flag. P.
C, Mahalanobis,
the great Indian scientist, was
spellbound.
xiv
τ*
Contents
23.
Eighty Years of Trained Incapacity: How Such a Thing
Could Happen
238
R. A. Fisher was a necessary condition for the standard error of regressions. No Fisher, no
lasting error. But for null-hypothesis significance testing to persist in the face of its logical
and practical difficulties, something else must be operating. Perhaps it is what
Thorstein
Veblen called trained incapacity, to which might be added what Robert Merton called
the bureaucratization of knowledge and what
Friedrich
Hayek called the scientistic prej¬
udice. We suggest that the sizeless sciences need to reform their scientistic bureaucracies.
24.
What to Do
245
What, then? Get back to size in science, and to real error seriously considered. It is more
difficult than Fisherian procedures, and cannot be reduced to mechanical procedures. How
big is big is a necessary question in any science and has no answer independent of the con¬
versation of scientists. But it has the merit at least of being relevant to science, business, and
life. The Fisherian procedures are not.
A Reader s Guide
253
Notes
255
Works Cited
265
Index
289
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| id | DE-604.BV035447426 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T21:35:29Z |
| institution | BVB |
| isbn | 9780472070077 9780472050079 047207007X 0472050079 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-017367550 |
| oclc_num | 263410925 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-20 DE-945 DE-19 DE-BY-UBM DE-188 DE-473 DE-BY-UBG DE-11 DE-703 DE-384 DE-824 |
| owner_facet | DE-355 DE-BY-UBR DE-20 DE-945 DE-19 DE-BY-UBM DE-188 DE-473 DE-BY-UBG DE-11 DE-703 DE-384 DE-824 |
| physical | XXIII, 321 S. Ill., graph. Darst. |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Univ. of Michigan Press |
| record_format | marc |
| series2 | Economics, cognition, and society series |
| spelling | Ziliak, Stephen T. 1963- Verfasser (DE-588)129252891 aut The cult of statistical significance how the standard error costs us jobs, justice, and lives by Stephen T. Ziliak and Deirdre N. McCloskey Ann Arbor Univ. of Michigan Press 2008 XXIII, 321 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Economics, cognition, and society series Literaturverz. S. 265 - 287 Fisher, Ronald Aylmer 1890-1962 (DE-588)11853355X gnd rswk-swf Gosset, William Sealy 1876-1937 (DE-588)118984098 gnd rswk-swf Gesellschaft Statistik Wirtschaft Economics Statistical methods Statistics Social aspects Statistical hypothesis testing Social aspects Signifikanzwahrscheinlichkeit (DE-588)4181281-5 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Wirtschaftsstatistik (DE-588)4066517-3 gnd rswk-swf Wirtschaftsstatistik (DE-588)4066517-3 s Signifikanzwahrscheinlichkeit (DE-588)4181281-5 s DE-604 Gosset, William Sealy 1876-1937 (DE-588)118984098 p Statistik (DE-588)4056995-0 s Fisher, Ronald Aylmer 1890-1962 (DE-588)11853355X p McCloskey, Deirdre N. 1942- Verfasser (DE-588)12325972X aut Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017367550&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Ziliak, Stephen T. 1963- McCloskey, Deirdre N. 1942- The cult of statistical significance how the standard error costs us jobs, justice, and lives Fisher, Ronald Aylmer 1890-1962 (DE-588)11853355X gnd Gosset, William Sealy 1876-1937 (DE-588)118984098 gnd Gesellschaft Statistik Wirtschaft Economics Statistical methods Statistics Social aspects Statistical hypothesis testing Social aspects Signifikanzwahrscheinlichkeit (DE-588)4181281-5 gnd Statistik (DE-588)4056995-0 gnd Wirtschaftsstatistik (DE-588)4066517-3 gnd |
| subject_GND | (DE-588)11853355X (DE-588)118984098 (DE-588)4181281-5 (DE-588)4056995-0 (DE-588)4066517-3 |
| title | The cult of statistical significance how the standard error costs us jobs, justice, and lives |
| title_auth | The cult of statistical significance how the standard error costs us jobs, justice, and lives |
| title_exact_search | The cult of statistical significance how the standard error costs us jobs, justice, and lives |
| title_full | The cult of statistical significance how the standard error costs us jobs, justice, and lives by Stephen T. Ziliak and Deirdre N. McCloskey |
| title_fullStr | The cult of statistical significance how the standard error costs us jobs, justice, and lives by Stephen T. Ziliak and Deirdre N. McCloskey |
| title_full_unstemmed | The cult of statistical significance how the standard error costs us jobs, justice, and lives by Stephen T. Ziliak and Deirdre N. McCloskey |
| title_short | The cult of statistical significance |
| title_sort | the cult of statistical significance how the standard error costs us jobs justice and lives |
| title_sub | how the standard error costs us jobs, justice, and lives |
| topic | Fisher, Ronald Aylmer 1890-1962 (DE-588)11853355X gnd Gosset, William Sealy 1876-1937 (DE-588)118984098 gnd Gesellschaft Statistik Wirtschaft Economics Statistical methods Statistics Social aspects Statistical hypothesis testing Social aspects Signifikanzwahrscheinlichkeit (DE-588)4181281-5 gnd Statistik (DE-588)4056995-0 gnd Wirtschaftsstatistik (DE-588)4066517-3 gnd |
| topic_facet | Fisher, Ronald Aylmer 1890-1962 Gosset, William Sealy 1876-1937 Gesellschaft Statistik Wirtschaft Economics Statistical methods Statistics Social aspects Statistical hypothesis testing Social aspects Signifikanzwahrscheinlichkeit Wirtschaftsstatistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017367550&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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