Astronomy is perhaps the oldest observational science
^{(1)}. The effort to
understand the mysterious luminous objects in the sky has been an
important element of human culture for at least 10^{4} years.
Quantitative measurements of celestial phenomena were carried out
by many ancient civilizations. The classical Greeks were not
active observers but were unusually creative in the applications
of mathematical principles to astronomy. The geometric models of
the Platonists with crystalline spheres spinning around the static
Earth were elaborated in detail, and this model endured in Europe
for 15 centuries. But it was another Greek natural philosopher,
Hipparchus, who made one of the first applications of mathematical
principles that we now consider to be in the realm of statistics.
Finding scatter in Bablylonian measurements of the length of a
year, defined as the time between solstices, he took the middle of
the range - rather than the mean or median - for the best value.

This is but one of many discussions of statistical issues in the history of astronomy. Ptolemy estimated parameters of a non-linear cosmological model using a minimax goodness-of-fit method. Al-Biruni discussed the dangers of propagating errors from inaccurate instruments and inattentive observers. While some Medieval scholars advised against the acquisition of repeated measurements, fearing that errors would compound rather than compensate for each other, the usefulnes of the mean to increase precision was demonstrated with great success by Tycho Brahe.

During the 19th century, several elements of modern mathematical
statistics were developed in the context of celestial mechanics,
where the application of Newtonian theory to solar system
phenomena gave astonishingly precise and self-consistent
quantitative inferences. Legendre developed *L*_{2} least
squares parameter estimation to model cometary orbits. The least-squares
method became an instant success in European astronomy and
geodesy. Other astronomers and physicists contributed to
statistics: Huygens wrote a book on probability in games of
chance; Newton developed an interpolation procedure; Halley laid
foundations of actuarial science; Quetelet worked on statistical
approaches to social sciences; Bessel first used the concept of
"probable error"; and Airy wrote a volume on the theory of errors.

But the two fields diverged in the late-19th and 20th centuries.
Astronomy leaped onto the advances of physics - electromagnetism,
thermodynamics, quantum mechanics and general relativity - to
understand the physical nature of stars, galaxies and the Universe
as a whole. A subfield called "statistical astronomy" was still
present but concentrated on rather narrow issues involving star
counts and Galactic structure
[30].
Statistics
concentrated on analytical approaches. It found its principle
applications in social sciences, biometrical sciences and in
practical industries (*e.g.*, Sir R. A. Fisher's employment
by the British agricultural service).

^{1} The
historical relationship between astronomy and statistics is
described in references
[15],
[38]
and elsewhere. Our *Astrostatistics* monograph gives more detail
and contemporary examples of astrostatistical problems
[3].
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