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96 MATHEMATICS MAGAZINE
The Evolution of the Normal Distribution
SAUL STAHL
Department of Mathematics
University of Kansas
Lawrence, KS 66045, USA
stahl@math.ku.edu
Statistics is the most widely applied of all mathematical disciplines and at the center
of statistics lies the normal distribution, known to millions of people as the bell curve,
or the bellshaped curve. This is actually a twoparameter family of curves that are
graphs of the equation
y =
1
√
2πσ
e
−
1
2
x−µ
σ
2
(1)
Several of these curves appear in F
IGURE 1. Not only is the bell curve familiar to these
millions, but they also know of its main use: to describe the general, or idealized, shape
of graphs of data. It has, of course, many other uses and plays as signiﬁcant a role in
the social sciences as differentiation does in the natural sciences. As is the case with
many important mathematical concepts, the rise of this curve to its current prominence
makes for a tale that is both instructive and amusing. Actually, there are two tales here:
the invention of the curve as a tool for computing probabilities and the recognition of
its utility in describing data sets.
10
5 5
10
15
0.2
0.4
0.6
0.8
x
y
Figure 1 Bellshaped curves
An approximation tool
The origins of the mathematical theory of probability are justly attributed to the fa
mous correspondence between Fermat and Pascal, which was instigated in 1654 by
the queries of the gambling Chevalier de M
´
er
´
e[6]. Among the various types of prob
lems they considered were binomial distributions, which today would be described by
VOL. 79, NO. 2, APRIL 2006 97
such sums as
j
k=i
n
k
p
k
(1 − p)
n−k
. (2)
This sum denotes the likelihood of between i and j successes in n trials with success
probability p. Such a trial—now called a Bernoulli trial—is the most elementary of
all random experiments. It has two outcomes, usually termed success and failure.The
kth term in (2) is the probability that k of the n trials are successful.
As the binomial examples Fermat and Pascal worked out involved only small values
of n, they were not concerned with the computational challenge presented by the eval
uation of general sums of this type. However, more complicated computations were
not long in coming.
For example, in 1712 the Dutch mathematician ’sGravesande tested the hypothesis
that male and female births are equally likely against the actual births in London over
the 82 years 1629–1710 [14, 16]. He noted that the relative number of male births
varies from a low of 7765/15, 448 = 0.5027 in 1703 to a high of 4748/8855 = 0.5362
in 1661. ’sGravesande multiplied these ratios by 11,429, the average number of births
over this 82 year span. These gave him nominal bounds of 5745 and 6128 on the
number of male births in each year. Consequently, the probability that the observed
excess of male births is due to randomness alone is the 82nd power of
Pr
5745 ≤ x ≤ 6128  p =
1
2
=
6128
x=5745
11,429
x
1
2
11,429
≈
3,849,150
13,196,800
≈ 0.292
(Hald explains the details of this rational approximation [16].) ’sGravesande did make
use of the recursion
n
x + 1
=
n
x
n − x
x + 1
suggested by Newton for similar purposes, but even so this is clearly an onerous task.
Since the probability of this difference in birth rates recurring 82 years in a row is
the extremely small number 0.292
82
, ’sGravesande drew the conclusion that the higher
male birth rates were due to divine intervention.
A few years earlier Jacob Bernoulli had found estimates for binomial sums of the
type of (2). These estimates, however, did not involve the exponential function e
x
.
De Moivre began his search for such approximations in 1721. In 1733, he proved
[16, 25]that
n
n
2
+ d
1
2
n
≈
2
√
2πn
e
−2d
2
/n
(3)
and
x −n/2≤d
n
x
1
2
n
≈
4
√
2π
d/
√
n
0
e
−2y
2
dy. (4)
De Moivre also asserted that (4) could be generalized to a similar asymmetrical con
text, with x varying from n/2tod + n/2. This is easily done, with the precision of the
approximation clariﬁed by De Moivre’s proof.
98 MATHEMATICS MAGAZINE
10 20 30 40 50
k
0.02
0.04
0.06
0.08
0.10
0.12
p
Figure 2 An approximation of binomial probabilities
FIGURE 2 demonstrates how the binomial probabilities associated with 50 inde
pendent repetitions of a Bernoulli trial with probability p = 0.3 of success are ap
proximated by such a exponential curve. De Moivre’s discovery is standard fare in
all introductory statistics courses where it is called the normal approximation to the
binomial and rephrased as
j
i
p
k
p
k
(1 − p)
n−k
≈ N
j − np
√
np(1 − p)
− N
i − np
√
np(1 − p)
where
N(z) =
1
√
2π
z
−∞
e
−x
2
/2
dx
Since this integral is easily evaluated by numerical methods and quite economically
described by tables, it does indeed provide a very practical approximation for cumula
tive binomial probabilities.
The search for an error curve
Astronomy was the ﬁrst science to call for accurate measurements. Consequently, it
was also the ﬁrst science to be troubled by measurement errors and to face the question
of how to proceed in the presence of several distinct observations of the same quantity.
In the 2nd century
BC, Hipparchus seems to have favored the midrange. Ptolemy, in the
2nd century
AD, when faced with several discrepant estimates of the length of a year,
may have decided to work with the observation that ﬁt his theory best [29]. Towards
the end of the 16th century, Tycho Brahe incorporated the repetition of measurements
into the methodology of astronomy. Curiously, he failed to specify how these repeated
observations should be converted into a single number. Consequently, astronomers de
vised their own, often ad hoc, methods for extracting a mean, or data representative,
out of their observations. Sometimes they averaged, sometimes they used the median,
sometimes they grouped their data and resorted to both averages and medians. Some
times they explained their procedures, but often they did not. Consider, for example,
the following excerpt, which comes from Kepler [19] and reports observations made,
in fact, by Brahe himself:
VOL. 79, NO. 2, APRIL 2006 99
On 1600 January 13/23 at 11
h
50
m
the right ascension of Mars was:
◦
using the bright foot of Gemini 134 23 39
using Cor Leonis 134 27 37
using Pollux 134 23 18
at 12
h
17
m
, using the third in the wing of Virgo 134 29 48
The mean, treating the observations impartially: 134 24 33
Kepler’s choice of data representative is bafﬂing. Note that
Average: 134
◦
26
5.5
Median: 134
◦
25
38
and it is difﬁcult to believe that an astronomer who recorded angles to the nearest sec
ond could fail to notice a discrepancy larger than a minute. The consensus is that the
chosen mean could not have been the result of an error but must have been derived
by some calculations. The literature contains at least two attempts to reconstruct these
calculations [7, p. 356], [35] but this author ﬁnds neither convincing, since both expla
nations are ad hoc, there is no evidence of either ever having been used elsewhere, and
both result in estimates that differ from Kepler’s by at least ﬁve seconds.
To the extent that they recorded their computation of data representatives, the as
tronomers of the time seem to be using improvised procedures that had both averages
and medians as their components [7, 29, 30]. The median versus average controversy
lasted for several centuries and now appears to have been resolved in favor of the latter,
particularly in scientiﬁc circles. As will be seen from the excerpts below, this decision
had a strong bearing on the evolution of the normal distribution.
The ﬁrst scientist to note in print that measurement errors are deserving of a sys
tematic and scientiﬁc treatment was Galileo in his famous Dialogue Concerning the
Two Chief Systems of the World—Ptolemaic and Copernican [9], published in 1632.
His informal analysis of the properties of random errors inherent in the observations
of celestial phenomena is summarized by Stigler [16], in ﬁve points:
1. There is only one number which gives the distance of the star from the center of the
earth, the true distance.
2. All observations are encumbered with errors, due to the observer, the instruments,
and the other observational conditions.
3. The observations are distributed symmetrically about the true value; that is the er
rors are distributed symmetrically about zero.
4. Small errors occur more frequently than large errors.
5. The calculated distance is a function of the direct angular observations such that
small adjustments of the observations may result in a large adjustment of the dis
tance.
Unfortunately, Galileo did not address the question of how the true distance should
be estimated. He did, however, assert that: “ ... it is plausible that the observers are
more likely to have erred little than much ... ”[9, p. 308]. It is therefore not unrea
sonable to attribute to him the belief that the most likely true value is that which mini
mizes the sum of its deviations from the observed values. (That Galileo believed in the
straightforward addition of deviations is supported by his calculations on pp. 307–308
of the Dialogue [9].) In other words, faced with the observed values x
1
, x
2
,... ,x
n
,
Galileo would probably have agreed that the most likely true value is the x that mini
mizes the function
100 MATHEMATICS MAGAZINE
f (x) =
n
n=1
x − x
i
 (5)
As it happens, this minimum is well known to be the median of x
1
, x
2
,... ,x
n
and not
their average, a fact that Galileo was likely to have found quite interesting.
This is easily demonstrated by an inductive argument [20], which is based on the
observation that if these values are reindexed so that x
1
< x
2
< ···< x
n
,then
n
i=1

x − x
i

=
n−1
i=2

x − x
i

+ (x
n
− x
1
) if x ∈[x
1
, x
n
],
whereas
n
i=1

x − x
i

>
n−1
i=2

x − x
i

+ (x
n
− x
1
) if x /∈[x
1
, x
n
].
It took hundreds of years for the average to assume the near universality that it
now possesses and its slow evolution is quite interesting. Circa 1660, we ﬁnd Robert
Boyle, later president of the Royal Society, arguing eloquently against the whole idea
of repeated experiments:
... experiments ought to be estimated by their value, not their number; ... a
single experiment ... may as well deserve an entire treatise. ... As one of those
large and orient pearls ... may outvalue a very great number of those little ...
pearls, that are to be bought by the ounce ...
In an article that was published posthumously in 1722 [5], Roger Cotes made the
following suggestion:
Let p be the place of some object deﬁned by observation, q, r, s, the places of the
same object from subsequent observations. Let there also be weights P, Q, R, S
reciprocally proportional to the displacements which may arise from the errors
in the single observations, and which are given from the given limits of error;
and the weights P, Q, R, S are conceived as being placed at p, q, r, s,andtheir
center of gravity Z is found: I say the point Z is the most probable place of the
object, and may be safely had for its true place.
Cotes apparently visualized the observations as tokens x
1
, x
2
,... ,x
n
(= p, q, r ,
s,...)with respective physical weights w
1
, w
2
, ..., w
n
(= P, Q, R, S,...) lined up
on a horizontal axis. F
IGURE 3 displays a case where n = 4 and all the tokens have
equal weight. When Cotes did this, it was natural for him to suggest that the center
of gravity Z of this system should be designated to represent the observations. After
all, in physics too, a body’s entire mass is assumed to be concentrated in its center
1 23 4
x
Z
x
x
x
Figure 3 A well balanced explanation of the average
VOL. 79, NO. 2, APRIL 2006 101
of gravity and so it could be said that the totality of the body’s points are represented
by that single point. That Cotes’s proposed center of gravity agrees with the weighted
average can be argued as follows. By the deﬁnition of the center of gravity, if the axis
is pivoted at Z it will balance and hence, by Archimedes’s law of the lever,
n
i=1
w
i
(Z − x
i
) = 0
or
Z =
n
i=1
w
i
x
i
n
i=1
w
i
(6)
Of course, when the weights w
i
are all equal, Z becomes the classical average
¯x =
1
n
n
i=1
x
i
.
It has been suggested that this is an early appearance of the method of least squares
[26]. In this context, the method proposes that we represent the data x
1
, x
2
,... ,x
n
by
the x that minimizes the function
g(x) =
n
i=1
w
i
(x − x
i
)
2
(7)
Differentiation with respect to x makes it clear that it is the Z of (6) that provides
this minimum. Note that the median minimizes the function f (x) of (5) whereas the
(weighted) average minimizes the function g(x) of (7). It is curious that each of the
two foremost data representatives can be identiﬁed as the minimizer of a nonobvious,
though fairly natural, function. It is also frustrating that so little is known about the
history of this observation.
Thomas Simpson’s paper of 1756 [36] is of interest here for two reasons. First
comes his opening paragraph:
It is well known to your Lordship, that the method practiced by astronomers, in
order to diminish the errors arising from the imperfections of instruments, and
of the organs of sense, by taking the Mean of several observations, has not been
generally received, but that some persons, of considerable note, have been of
opinion, and even publickly maintained, that one single observation, taken with
due care, was as much to be relied on as the Mean of a great number.
Thus, even as late as the mid18th century doubts persisted about the value of repe
tition of experiments. More important, however, was Simpson’s experimentation with
speciﬁc error curves—probability densities that model the distribution of random er
rors. In the two propositions, Simpson [36] computed the probability that the error in
the mean of several observations does not exceed a given bound when the individual
errors take on the values
−v,... ,−3, −2, −1, 0, 1, 2, 3,... ,v
with probabilities that are proportional to either
r
−v
,... ,r
−3
, r
−2
, r
−1
, r
0
, r
1
, r
2
, r
3
,... ,r
v
102 MATHEMATICS MAGAZINE
or
r
−v
, 2r
1−v
, 3r
2−v
... ,(v+ 1)r
0
... ,3r
v−2
, 2r
v−1
, r
v
Simpson’s choice of error curves may seem strange, but they were in all likelihood
dictated by the state of the art of probability at that time. For r = 1 (the simplest case),
these two distributions yield the two top graphs of F
IGURE 4. One year later, Simpson,
while effectively inventing the notion of a continuous error distribution, dealt with
similar problems in the context of the error curves described in the bottom of F
IGURE
4[37].
y
x
y
x
y
x
Figure 4 Simpson’s error curves
In 1774, Laplace proposed the ﬁrst of his error curves [21]. Denoting this function
by φ(x), he stipulated that it must be symmetric in x and monotone decreasing for
x > 0. Furthermore, he proposed that
... as we have no reason to suppose a different law for the ordinates than for
their differences, it follows that we must, subject to the rules of probabilities,
suppose the ratio of two inﬁnitely small consecutive differences to be equal to
that of the corresponding ordinates. We thus will have
dφ(x + dx)
dφ(x)
=
φ(x + dx)
φ(x)
Therefore
dφ(x)
dx
=−mφ(x).
... Therefore
φ(x) =
m
2
e
−mx
.
Laplace’s argument can be paraphrased as follows. Aside from their being symmet
rical and descending (for x > 0), we know nothing about either φ(x) or φ
(x). Hence,
presumably by Occam’s razor, it must be assumed that they are proportional (the sim
pler assumption of equality leads to φ(x) = Ce
x 
, which is impossible). The resulting
differential equation is easily solved and the extracted error curve is displayed in F
IG
URE 5. There is no indication that Laplace was in any way disturbed by this curve’s
VOL. 79, NO. 2, APRIL 2006 103
m/2
y
x
Figure 5 Laplace’s ﬁrst error curve
nondifferentiability at x = 0. We are about to see that he was perfectly willing to en
tertain even more drastic singularities.
Laplace must have been aware of the shortcomings of his rationale, for three short
years later he proposed an alternative curve [23]. Let a be the supremum of all the
possible errors (in the context of a speciﬁc experiment) and let n be a positive integer.
Choose n points at random within the unit interval, thereby dividing it into n + 1
spacings. Order the spacings as:
d
1
> d
2
> ···> d
n+1
, d
1
+ d
2
+···+d
n+1
= 1.
Let
¯
d
i
be the expected value of d
i
. Draw the points (i/n,
¯
d
i
), i = 1, 2,... ,n + 1and
let n become inﬁnitely large. The limit conﬁguration is a curve that is proportional to
ln(a/x) on (0, a]. Symmetry and the requirement that the total probability must be 1
then yield Laplace’s second candidate for the error curve (F
IGURE 6):
y =
1
2a
ln
a
x
− a ≤ x ≤ a.
a
a
y
x
Figure 6 Laplace’s second error curve
This curve, with its inﬁnite singularity at 0 and ﬁnite domain (a reversal of the
properties of the error curve of F
IGURE 5 and the bellshaped curve) constitutes a step
104 MATHEMATICS MAGAZINE
backwards in the evolutionary process and one suspects that Laplace was seduced by
the considerable mathematical intricacies of the curve’s derivation. So much so that
he seemed compelled to comment on the curve’s excessive complexity and to sug
gest that error analyses using this curve should be carried out only in “very delicate”
investigations, such as the transit of Venus across the sun.
Shortly thereafter, in 1777, Daniel Bernoulli wrote [2]:
Astronomers as a class are men of the most scrupulous sagacity; it is to them
therefore that I choose to propound these doubts that I have sometimes enter
tained about the universally accepted rule for handling several slightly discrepant
observations of the same event. By these rules the observations are added to
gether and the sum divided by the number of observations; the quotient is then
accepted as the true value of the required quantity, until better and more certain
information is obtained. In this way, if the several observations can be considered
as having, as it were, the same weight, the center of gravity is accepted as the
true position of the object under investigation. This rule agrees with that used in
the theory of probability when all errors of observation are considered equally
likely.
But is it right to hold that the several observations are of the same weight or
moment or equally prone to any and every error? Are errors of some degrees as
easy to make as others of as many minutes? Is there everywhere the same proba
bility? Such an assertion would be quite absurd, which is undoubtedly the reason
why astronomers prefer to reject completely observations which they judge to be
too wide of the truth, while retaining the rest and, indeed, assigning to them the
same reliability.
It is interesting to note that Bernoulli acknowledged averaging to be universally
accepted. As for the elusive error curve, he took it for granted that it should have a
ﬁnite domain and he was explicit about the tangent being horizontal at the maximum
point and almost vertical near the boundaries of the domain. He suggested the semi
ellipse as such a curve, which, following a scaling argument, he then replaced with a
semicircle.
The next important development had its roots in a celestial event that occurred on
January 1, 1801. On that day the Italian astronomer Giuseppe Piazzi sighted a heavenly
body that he strongly suspected to be a new planet. He announced his discovery and
named it Ceres. Unfortunately, six weeks later, before enough observations had been
taken to make possible an accurate determination of its orbit, so as to ascertain that
it was indeed a planet, Ceres disappeared behind the sun and was not expected to
reemerge for nearly a year. Interest in this possibly new planet was widespread and
astronomers throughout Europe prepared themselves by compuguessing the location
where Ceres was most likely to reappear. The young Gauss, who had already made a
name for himself as an extraordinary mathematician, proposed that an area of the sky
be searched that was quite different from those suggested by the other astronomers
and he turned out to be right. An article in the 1999 M
AGAZINE [42] tells the story in
detail.
Gauss explained that he used the least squares criterion to locate the orbit that best
ﬁt the observations [12]. This criterion was justiﬁed by a theory of errors that was
based on the following three assumptions:
1. Small errors are more likely than large errors.
2. For any real number the likelihood of errors of magnitudes and − are equal.
3. In the presence of several measurements of the same quantity, the most likely value
of the quantity being measured is their average.
VOL. 79, NO. 2, APRIL 2006 105
On the basis of these assumptions he concluded that the probability density for the
error (that is, the error curve) is
φ(x) =
h
√
π
e
−h
2
x
2
where h is a positive constant that Gauss thought of as the “precision of the mea
surement process”. We recognize this as the bell curve determined by µ = 0and
σ = 1/
√
2h.
Gauss’s ingenious derivation of this error curve made use of only some basic prob
abilistic arguments and standard calculus facts. As it falls within the grasp of under
graduate mathematics majors with a course in calculus based statistics, his proof is
presented here with only minor modiﬁcations.
The proof
Let p be the true (but unknown) value of the measured quantity, let n independent
observations yield the estimates M
1
, M
2
,... ,M
n
,andletφ(x) be the probability den
sity function of the random error. Gauss took it for granted that this function is dif
ferentiable. Assumption 1 above implies that φ(x) has a maximum at x = 0 whereas
Assumption 2 means that φ(−x) = φ(x).Ifwedeﬁne
f (x) =
φ
(x)
φ(x)
then
f (−x) =−f (x)
Note that M
i
− p denotes the error of the i th measurement and consequently, since
these measurements (and errors) are assumed to be stochastically independent, it fol
lows that
= φ(M
1
− p)φ(M
2
− p)...φ(M
n
− p)
is the joint density function for the n errors. Gauss interpreted Assumption 3 as saying,
in modern terminology, that
¯
M =
M
1
+ M
2
+···+M
n
n
is the maximum likelihood estimate of p. In other words, given the measurements
M
1
, M
2
,... ,M
n
, the choice p =
¯
M maximizes the value of . Hence,
0 =
∂
∂p
p=
¯
M
=−φ
(M
1
−
¯
M)φ(M
2
−
¯
M) ···φ(M
n
−
¯
M)
− φ(M
1
−
¯
M)φ
(M
2
−
¯
M) ···φ(M
n
−
¯
M) −···
− φ(M
1
−
¯
M)φ(M
2
−
¯
M) ···φ
(M
n
−
¯
M)
=−
φ
(M
1
−
¯
M)
φ(M
1
−
¯
M)
+
φ
(M
2
−
¯
M)
φ(M
2
−
¯
M)
+···+
φ
(M
n
−
¯
M)
φ(M
n
−
¯
M)
.
106 MATHEMATICS MAGAZINE
It follows that
f (M
1
−
¯
M) + f (M
2
−
¯
M) +···+ f (M
n
−
¯
M) = 0. (8)
Recall that the measurements M
i
can assume arbitrary values and in particular, if M
and N are arbitrary real numbers we may use
M
1
= M, M
2
= M
3
=···=M
n
= M − nN
for which set of measurements
¯
M = M − (n − 1)N .
Substitution into (8) yields
f ((n − 1)N ) + (n − 1) f (−N ) = 0or f ((n − 1)N) = (n − 1) f (N ).
It is a wellknown exercise that this homogeneity condition, when combined with the
continuity of f , implies that f (x) = kx for some real number k. This yields the dif
ferential equation
φ
(x)
φ(x)
= kx.
Integration with respect to x produces
ln φ(x) =
k
2
x
2
+ c or φ(x) = Ae
kx
2
/2
.
In order for φ(x) to assume a maximum at x = 0, k must be negative and so we may
set k/2 =−h
2
. Finally, since
∞
−∞
e
−h
2
x
2
dx =
√
π
h
it follows that
φ(x) =
h
√
π
e
−h
2
x
2
,
which completes the proof.
FIGURE 7 displays a histogram of some measurements of the right ascension of
Mars [32] together with an approximating exponential curve. The ﬁt is certainly
striking.
It was noted above that the average is in fact a least squares estimator of the data.
This means that Gauss used a particular least squares estimation to justify his theory of
errors which in turn was used to justify the general least squares criterion. There is an
element of bootstrapping in this reasoning that has left later statisticians dissatisﬁed
and may have had a similar effect on Gauss himself. He returned to the subject twice,
twelve and thirty years later, to explain his error curve by means of different chains of
reasoning.
Actually, a highly plausible explanation is implicit in the Central Limit Theorem
published by Laplace in 1810 [22]. Laplace’s translated and slightly paraphrased state
ment is:
... if it is assumed that for each observation the positive and negative errors
are equally likely, the probability that the mean error of n observations will be
VOL. 79, NO. 2, APRIL 2006 107
3 2
1
0
1
2 3
0.05
0.1
0.15
0.2
Deviation from
mean observation
Relative
frequency
Figure 7 Normally distributed measurements
contained within the bounds ±rh/n, equals
2
√
π
·
k
2k
·
dr · e
−
k
2k
·r
2
where h is the interval within which the errors of each observation can fall. If
the probability of error ±x is designated by φ(x/h),thenk is the integral
dx ·
φ(x/h) evaluated from x =−
1
2
h to x =
1
2
h,andk
is the integral
x
2
h
2
· dx ·
φ(x/h) evaluated in the same interval.
Loosely speaking, Laplace’s theorem states that if the error curve of a single obser
vation is symmetric, then the error curve of the sum of several observations is indeed
approximated by one of the Gaussian curves of (1). Hence if we take the further step
of imagining that the error involved in an individual observation is the aggregate of
a large number of “elementary” or “atomic” errors, then this theorem predicts that
the random error that occurs in that individual observation is indeed controlled by De
Moivre and Gauss’s curve (1).
This assumption, promulgated by Hagen [15] and Bessel [4], became known as the
hypothesis of elementary errors. A supporting study had already been carried out by
Daniel Bernoulli in 1780 [3], albeit one of much narrower scope. Assuming a ﬁxed
error ±α for each oscillation of a pendulum clock, Bernoulli concluded that the ac
cumulated error over, say, a day, would be, in modern terminology, approximately
normally distributed.
This might be the time to recapitulate the average’s rise to the prominence it now
enjoys as the estimator of choice. Kepler’s treatment of his observations shows that
around 1600 there still was no standard procedure for summarizing multiple observa
tions. Around 1660 Boyle still objected to the idea of combining several measurements
into a single one. Half a century later, Cotes proposed the average as the best estima
tor. Simpson’s article of 1756 indicates that the opponents of the process of averaging,
while apparently a minority, had still not given up. Bernoulli’s article of 1777 admit
ted that the custom of averaging had become universal. Finally, some time in the ﬁrst
decade of the 19th century, Gauss assumed the optimality of the average as an axiom
for the purpose of determining the distribution of measurement errors.
108 MATHEMATICS MAGAZINE
Beyond errors
The ﬁrst mathematician to extend the provenance of the normal distribution beyond
the distribution of measurement errors was Adolphe Quetelet (1796–1874). He began
his career as an astronomer but then moved on to the social sciences. Consequently,
he possessed an unusual combination of qualiﬁcations that placed him in just the right
position for him to be able to make one of the most inﬂuential scientiﬁc observations
of all times.
TABLE 1: Chest measurements of
Scottish soldiers
Girth Frequency
33 3
34 18
35 81
36 185
37 420
38 749
39 1,073
40 1,079
41 934
42 658
43 370
44 92
45 50
46 21
47 4
48 1
5,738
In his 1846 book Letters addressed to H. R. H. the grand duke of Saxe Coburg and
Gotha, on the Theory of Probabilities as Applied to the Moral and Political Sciences
[32, p. 400], Quetelet extracted the contents of Table 1 from the Edinburgh Medical
and Surgical Journal (1817) and contended that the pattern followed by the variety of
its chest measurements was identical with that formed by the type of repeated measure
ments that are so common in astronomy. In modern terminology, Quetelet claimed that
the chest measurements of Table 1 were normally distributed. Readers are left to draw
their own conclusions regarding the closeness of the ﬁt attempted in F
IGURE 8. The
more formal χ
2
normality test yields a χ
2
Tes t
value of 47.1, which is much larger than
the cutoff value of χ
2
10,.05
= 18.3, meaning that by modern standards these data cannot
be viewed as being normally distributed. (The number of bins was reduced from 16
to 10 because six of them are too small.) This discrepancy indicates that Quetelet’s
justiﬁcation of his claim of the normality the chest measurements merits a substantial
dose of skepticism. It appears here in translation [32, Letter XX]:
I now ask if it would be exaggerating, to make an even wager, that a person
little practiced in measuring the human body would make a mistake of an inch
in measuring a chest of more than 40 inches in circumference? Well, admitting
this probable error, 5,738 measurements made on one individual would certainly
VOL. 79, NO. 2, APRIL 2006 109
33 35 37 39
41
43
45 47
0.05
0.1
0.15
0.2
Relative
frequency
Ches
t
girth
Figure 8 Is this data normally distributed?
not group themselves with more regularity, as to the order of magnitude, than
the 5,738 measurements made on the scotch [sic] soldiers; and if the two se
ries were given to us without their being particularly designated, we should be
much embarrassed to state which series was taken from 5,738 different soldiers,
and which was obtained from one individual with less skill and ruder means of
appreciation.
This argument, too, is unconvincing. It would have to be a strange person indeed
who could produce results that diverge by 15
while measuring a chest of girth 40
.
Any idiosyncracy or unusual conditions (fatigue, for example), that would produce
such unreasonable girths is more than likely to skew the entire measurement process
to the point that the data would fail to be normal.
It is interesting to note that Quetelet was the man who coined the phrase the average
man. In fact, he went so far as to view this mythical being as an ideal form whose
various corporeal manifestations were to be construed as measurements that are beset
with errors [34, p. 99]:
If the average man were completely determined, we might ... consider him as
the type of perfection; and everything differing from his proportions or condi
tion, would constitute deformity and disease; everything found dissimilar, not
only as regarded proportion or form, but as exceeding the observed limits, would
constitute a monstrosity.
Quetelet was quite explicit about the application of this, now discredited, principle
to the Scottish soldiers. He takes the liberty of viewing the measurements of the sol
diers’ chests as a repeated estimation of the chest of the average soldier:
I will astonish you in saying that the experiment has been done. Yes, truly, more
than a thousand copies of a statue have been measured, and though I will not
assert it to be that of the Gladiator, it differs, in any event, only slightly from
it: these copies were even living ones, so that the measures were taken with all
possible chances of error, I will add, moreover, that the copies were subject to
deformity by a host of accidental causes. One may expect to ﬁnd here a consid
erable probable error [32, p. 136].
110 MATHEMATICS MAGAZINE
Finally, it should be noted that TABLE 1 contains substantial errors. The original
data was split amongst tables for eleven local militias, crossclassiﬁed by height and
chest girth, with no marginal totals, and Quetelet made numerous mistakes in extract
ing his data. The actual counts are displayed in T
ABLE 2 where they are compared to
Quetelet’s counts.
TABLE 2: Chest measurements of Scottish soldiers
Actual Quetelet’s
Girth frequency frequency
33 3 3
34 19 18
35 81 81
36 189 185
37 409 420
38 753 749
39 1,062 1,073
40 1,082 1,079
41 935 934
42 646 658
43 313 370
44 168 92
45 50 50
46 18 21
47 3 4
48 1 1
5,738 5,732
Quetelet’s book was very favorably reviewed in 1850 by the eminent and eclectic
British scientist John F. W. Herschel [18]. This extensive review contained the outline
of a different derivation of Gauss’s error curve, which begins with the following three
assumptions:
1. ... the probability of the concurrence of two or more independent simple events,
is the product of the probabilities of its constituents considered singly;
2. ... the greater the error the less its probability ...
3. ... errors are equally probable if equal in numerical amount ...
Herschel’s third postulate is much stronger than the superﬁcially similar symmetry
assumption of Galileo and Gauss. The latter is onedimensional and is formalized as
φ() = φ(−) whereas the former is multidimensional and is formalized as asserting
the existence of a function ψ such that
φ(x)φ(y) ···φ(t) = ψ(x
2
+ y
2
+···+t
2
).
Essentially the same derivation had already been published by the American
R. Adrain in 1808 [1], prior to the publication of Gauss’s paper [12] but subse
quent to the location of Ceres. In his 1860 paper on the kinetic theory of gases [24],
the renowned British physicist J. C. Maxwell repeated the same argument and used it,
in his words:
To ﬁnd the average number of particles whose velocities lie between given limits,
after a great number of collisions among a great number of equal particles.
VOL. 79, NO. 2, APRIL 2006 111
The social sciences were not slow to realize the value of Quetelet’s discovery to
their respective ﬁelds. The American Benjamin A. Gould, the Italians M. L. Bodio
and Luigi Perozzo, the Englishman Samuel Brown, and the German Wilhelm Lexis
all endorsed it [31, p. 109]. Most notable amongst its proponents was the English gen
tleman and scholar Sir Francis Galton who continued to advocate it over the span of
several decades. This aspect of his career began with his 1869 book Hereditary Genius
[10, pp. 22–32] in which he sought to prove that genius runs in families. As he was
aware that exceptions to this rule abound, it had to be veriﬁed as a statistical, rather
than absolute, truth. What was needed was an efﬁcient quantitative tool for describ
ing populations and that was provided by Quetelet whose claims of the wide ranging
applicability of Gauss’s error curve Galton had encountered and adopted in 1863.
As the description of the precise use that Galton made of the normal curve would
take us too far aﬁeld, we shall only discuss his explanation for the ubiquity of the
normal distribution. In his words [11, p. 38]:
Considering the importance of the results which admit of being derived whenever
the law of frequency of error can be shown to apply, I will give some reasons
why its applicability is more general than might have been expected from the
highly artiﬁcial hypotheses upon which the law is based. It will be remembered
that these are to the effect that individual errors of observation, or individual
differences in objects belonging to the same generic group, are entirely due to
the aggregate action of variable inﬂuences in different combinations, and that
these inﬂuences must be
(1) all independent in their effects,
(2) all equal,
(3) all admitting of being treated as simple alternatives “above average” or “be
low average;”
(4) the usual Tables are calculated on the further supposition that the variable
inﬂuences are inﬁnitely numerous.
This is, of course, an informal restatement of Laplace’s Central Limit Theorem. The
same argument had been advanced by Herschel [18, p. 30]. Galton was fully aware that
conditions (14) never actually occur in nature and tried to show that they were unnec
essary. His argument, however, was vague and inadequate. Over the past two centuries
the Central Limit Theorem has been greatly generalized and a newer version exists,
known as Lindeberg’s Theorem [8], which makes it possible to dispense with require
ment (2). Thus, De Moivre’s curve (1) emerges as the limiting, observable distribution
even when the aggregated “atoms” possess a variety of nonidentical distributions. In
general it seems to be commonplace for statisticians to attribute the great success of
the normal distribution to these generalized versions of the Central Limit Theorem.
Quetelet’s belief that all deviations from the mean were to be regarded as errors in a
process that seeks to replicate a certain ideal has been relegated to the same dustbin
that contains the phlogiston and aether theories.
Why
normal
?
A word must be said about the origin of the term normal. Its aptness is attested by the
fact that three scientists independently initiated its use to describe the error curve
112 MATHEMATICS MAGAZINE
φ(x) =
1
√
2π
e
−x
2
/2
These were the American C. S. Peirce in 1873, the Englishman Sir Francis Galton in
1879, and the German Wilhelm Lexis, also in 1879 [41, pp. 407–415]. Its widespread
use is probably due to the inﬂuence of the great statistician Karl E. Pearson, who had
this to say in 1920 [27, p. 185]:
Many years ago [in 1893] I called the LaplaceGaussian curve the normal curve,
which name, while it avoids the international question of priority, has the disad
vantage of leading people to believe that all other distributions of frequency are
in one sense or another abnormal.
At ﬁrst it was customary to refer to Gauss’s error curve as the Gaussian curve and
Pearson, unaware of De Moivre’s work, was trying to reserve some of the credit of dis
covery to Laplace. By the time he realized his error the normal curve had become one
of the most widely applied of all mathematical tools and nothing could have changed
its name. It is quite appropriate that the name of the error curve should be based on a
misconception.
Acknowledgment. The author thanks his colleagues James Church and Ben Cobb for their helpfulness, Martha
Siegel for her encouragement, and the anonymous referees for their constructive critiques. Much of the informa
tion contained in this offering came from the excellent histories [16, 17, 39, 41].
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