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\noindent{\bf Jerzy NEYMAN}\\
b. 16 April 1894 - d. 5 August 1981
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\noindent{\bf Summary.} Neyman was one of the towering figures in the
rise of 20th century mathematical statistics, contributing to the
theory of statistical experimentation and sample surveys, but especially
to the development of the formal theory of tests and confidence
intervals.
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Jerzy Neyman was born of Polish parents in 1894, in Bendery, which has
been
variously labeled as Rumania, Ukraine, and Moldavia because of the
vicissitudes of border-drawing in Eastern Europe. From 1912 to 1917 he
studied mathematics at the University of Kharkov, where he went on to
receive a Masters degree in 1920 and then became a lecturer. In the
summer
of 1921, Neyman went to Bydgoszcz in northern Poland, as part of an
exchange
of nationals between Russia and Poland agreed to at the end of the
Russian-Polish War. There he worked as ``senior statistical assistant''
at
the National Agricultural Institute and he wrote two long papers on
agricultural experimentation that were published in 1923 in Polish
(Splawa-Neyman, 1990 [1923a], 1925 [1923b]). 

In 1924, Neyman obtained his doctorate degree from the University of
Warsaw,
using as a thesis the work done in Bydgoszcz. The papers prepared during
his
time at the National Agricultural Institute led to Neyman's later
contributions to experimental design (see Neyman et al. 1935) and
especially
sampling (Neyman, 1934) but were also responsible in part for Neyman's
1925
visit to University College London, to work in Karl Pearson's (q.v.)
Laboratory.


There Neyman began a decade-long collaboration with Pearson's son, 
Egon (q.v.), a
collaboration which yielded the formal theory of tests of hypotheses and
also led to Neyman's subsequent invention of confidence intervals. We
describe some these contributions in more detail in subsequent sections.

By 1934, after holding various teaching and research positions in
Poland,
Neyman moved to England to join Egon Pearson at University College as a
Senior Lecturer and then Reader. In 1937, at the invitation of W.
Edwards
Deming (q.v.), Neyman made a six-week visit to the United States where he
lectured on
sampling at the U. S. Department of Agriculture Graduate School (e.g.,
see
Neyman, 1952 for a revised version of these lectures) and at several
universities. This visit ultimately led to an offer for Neyman to join
the
University of California at Berkeley where he ultimately established a
leading department of mathematical statistics. Here he institutionalized
the
now famous Berkeley Symposia on Statistics and Probability, gathering
together the world's leading mathematical statisticians every five years
from 1945 to 1970.

Throughout his Berkeley years, problems related to the war effort, to
medicine, to weather modification, and to astronomy offered Neyman
practical
applications which inspired theoretical development. Neyman worked
closely
with students, supervising almost 40 Ph.D. dissertations at Berkeley.
He continued to work at Berkeley until his retirement in 1961, and he
remained
an active participant in the department until his death on August 5,
1981. 

Neyman had always had scientific interests much broader than narrowly
construed mathematical statistics; as noted above, he constantly used
practical problems as springboards to theoretical advances. But his
interests extended to the practical issues of formation of statistical
societies (he was a prime mover in the establishment of the Bernoulli
Society as a section of the International Statistical Institute) and to
science well beyond statistics. Before coming to the United States he
had
helped commemorate the 450th anniversary of Copernicus' birth. In 1973
he
helped mark Copernicus' 500th anniversary by organizing and editing a
volume
about modern-day Copernican revolutions entitled {\em The Heritage of
Copernicus: Theories "Pleasing to the Mind."}

Neyman received many honors, including the Guy Medal in Gold from the
Royal
Statistical Society and numerous honorary degrees. He was elected as a
member of the National Academy of Sciences in the United States and as a
foreign member of the Swedish and Polish Academies of Science and of the
Royal Society. For detailed biographical material on Neyman, see Lehmann
(1994) and especially Reid (1982). 
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\noindent{\bf Neyman on Experimentation and Sampling}

While Neyman is perhaps best known today for his contributions to
sampling
theory and for a system of inference, his earliest published work
concerned
the design of experiments. The parallels between the design elements of
sampling and experimentation are obvious in Neyman's 1923 paper on
experimental design. He conceptualized the assignment of treatments to
units
in an experiment as the drawing without replacement of balls from urns,
one
urn for each treatment. These urns had the special property that the
removal
of a ball (representing the outcome of an experimental unit) from one
urn
causes it to disappear from the other urns as well. Thus Neyman showed
that
when there is a finite pool of experimental units that need to be
assigned
to treatments, the random assignment of units to treatments is exactly
parallel to the random selection of a sample from a finite population.
Neyman was to take up the issues in experimentation again in a
controversial
1935 paper on Latin square designs read before the Royal Statistical
Society.
This work provoked a major dispute with R. A.. Fisher (q.v.) (see Fienberg and
Tanur (1996) for further details. 

During the 1920s there was much discussion of ``the method of
representative
sampling'' at the meetings of the International Statistical Institute
(ISI),
leading to a committee report which contained a description of two such
methods, random sampling (with all elements of the populations having
the
same probability of selection), and purposive selection of large groups
of
units (in modern terminology, clusters), chosen to match the population
on
selected control variates. Although the ISI report did not attempt to
choose
between the methods, Gini (q.v.) and Galvani subsequently presented a major
application
of purposive selection to the sampling of records from the 1921 Italian
census, in which they called into question the accuracy of random
sampling.

Neyman responded with his classic 1934 paper, in which he compared
purposive
and random sampling, but also offered elements of synthesis. He began by
describing stratified sampling, noting that earlier work by Bowley (q.v.)
considered only the
proportionate case. He then gave a crisp description of cluster sampling
explaining that the difficulties in sampling individuals
at random may be greatly diminished when we adopt groups
as the elements of sampling. In this new synthesis, he called this
procedure ``random sampling by groups.'' He then suggested combining
stratification with clustering to form ``random stratified sampling by
groups.''

The paper also contains a derivation of optimal allocation for
stratification, still known as Neyman allocation despite the later
discovery
of an earlier proof by Chuprov (q.v.). But perhaps the most notable feature
of
the paper at the time was Neyman's introduction of confidence intervals.
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\noindent{\bf The Neyman-Pearson Collaboration}

During the same period of time, Neyman entered into a collaboration with
Egon Pearson, which was to have a profound impact on the direction both
of
mathematical statistics and statistical practice for the rest of the
20th
century. Between 1926 and 1933, in response to what they viewed as
Fisher's ad
hoc approach to testing, they developed what we now know as the
Neyman-Pearson theory of hypothesis testing, which identifies an
alternative
hypothesis and recognizes two types of error. Their approach focused on
the
power to detect alternative hypotheses and led to the identification of
optimal test criteria in specific circumstances. It was to serve as the
foundation of much later work in mathematical statistics.

This work on testing also led Neyman to formulate his theory of
statistical
estimation via the method of confidence intervals, first introduced with
limited details in the 1934 sampling paper. As Neyman developed these
ideas
further in a 1937 paper, he linked the basic structure of tests and
interval
estimation in a fashion that allowed the carry-over of the
Neyman-Pearson
optimality results. The frequentist, repeated sampling interpretation of
confidence intervals remains difficult for many practicing scientists to
comprehend. 

Neyman continued to work on testing and estimation problems throughout
his
career and later developed an alternative to the Pearson chi-square
goodness-of-fit test, as well as the $C(\alpha)$ family of test
criteria,
and, in 1949, he used linearization methods to develop best
asymptotically
normal (BAN) estimates. 
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\begin{thebibliography}{3}

\bibitem{1}  Fienberg, S.E. and Tanur, J.M. (1996). Reconsidering the
fundamental contributions of Fisher and Neyman on experimentation and
sampling. {\em International Statistical Review}, {\bf 64}, 237--253. 

\bibitem{2}  Lehmann, E. L. (1994). Jerzy Neyman. 1894-1981. {\em %
Biographical Memoirs}, Vol. 63, National Academy Press, Washington, DC,
395--420. 

\bibitem{3}  Neyman, J. (1934). On two different aspects of the
representative method: the method of stratified sampling and the method
of
purposive selection (with discussion). {\em Journal of the Royal
Statistical
Society}, {\bf 97}, 558--625. 

\bibitem{4}  Neyman, J., with the cooperation of K. Iwaszkiewicz and St.
Kolodziejczyk. (1935).  Statistical problems in agricultural
experimentation (with discussion).  {\em Supplement to the Journal of
the
Royal Statistical Society}, {\bf 2}: 107--180.

\bibitem{5}  Neyman, J. (1937). Outline of a theory of statistical
estimation
based on the classical theory of probability. {\em Philosophical
Transactions
of the Royal Society of London, Series, A}, {\bf 236}, 333-380. 

\bibitem{6}  Neyman, J. (1952 [1938]). {\em Lectures and Conferences on
Mathematical Statistics and Probability}. U. S. Department of
Agriculture,
Washington, DC. (The 1952 edition is an expanded and revised version of
the
original 1938 mimeographed edition.) 

\bibitem{7}  Reid, C. (1982). {\em Neyman from Life}. Springer-Verlag,
New
York. 

\bibitem{8}  Splawa-Neyman, J. (1925[1923b]) Contributions of the theory
of small
samples drawn from a finite population. {\em Biometrika}, {\bf
17}: 472--479. (The note on this republication reads ``These results
with others were originally published in {\em La Revue Mensuelle de
Statistique}, publ. parl'Office Central de Statistique de la
R\'{e}publique Polonaise, tom.  vi. pp. 1--29, 1923.'') 

\bibitem{9}  Splawa-Neyman, J. (1990 [1923a]).  On the application of
probability
theory to agricultural experiments.  Essay on principles. Section 9.
{\em Statistical Science}, {\bf 5}: 465-472.  Translated and edited by
D. M.  Dabrowska and T. P. Speed from the Polish original, which
appeared in {\em Roczniki Nauk Rolniczyc}, {\bf Tom X} (1923): 1--51
({\em Annals of Agricultural Sciences}).
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\hfill{Stephen E. Fienberg and Judith M. Tanur}

\end{thebibliography}

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