If a bead bounces to the right
k times on its way down (and to the left on the remaining pegs) it ends up in the
kth bin counting from the left. Denoting the number of rows of pegs in a Galton Board by
n, the number of paths to the
kth bin on the bottom is given by the
binomial coefficient {n\choose k}. Note that the leftmost bin is the
0-bin, next to it is the
1-bin, etc. and the furthest one to the right is the
n-bin - making thus the total number of bins equal to
n+1 (each row does not need to have more pegs than the number that identifies the row itself, e.g. the first row has 1 peg, the second 2 pegs, until the
n-th row that has
n pegs which correspond to the
n+1 bins). If the probability of bouncing right on a peg is
p (which equals 0.5 on an unbiased level machine) the probability that the ball ends up in the
kth bin equals {n\choose k} p^k (1-p)^{n-k}. This is the probability mass function of a
binomial distribution. The number of rows correspond to the size of a binomial distribution in number of trials, while the probability
p of each pin is the binomial's
p. According to the
central limit theorem (more specifically, the
de Moivre–Laplace theorem), the binomial distribution approximates the normal distribution provided that the number of rows and the number of balls are both large. Varying the rows will result in different
standard deviations or widths of the bell-shaped curve or the
normal distribution in the bins. Another interpretation more accurate from the physical view is given by the
entropy: since the energy that is carried by every falling bead is finite, so even that on any tip their collisions are chaotic because the derivative is undefined (there is no way to previously figure out for which side is going to fall), the mean and variance of each bean is restricted to be finite (they will never bound out of the box), and the Gaussian shape arises because it is the
maximum entropy probability distribution for a continuous process with defined mean and variance. The rise of the
normal distribution could be interpreted as that all possible information carried by each bean related to which path it has travelled has been already completely lost through their downhill collisions. == Examples ==