This is the most important distribution in biometry because; most of the characters in agriculture, biology and genetics follow this distribution. The algebraic form of this distribution was first derived in 1733 by De Moivre (1667-1754) but this was later rediscovered in and developed by Gauss (280.9) and by Leplace (1812). For these reasons the normal distribution is also called the De Moivre, Gaussian or Leplace distribution.
Further, it is also known as Gaussion law of error because Gauss found that the errors of observations on any character follow the normal distribution. The term ‘normal’ is not used to mean the normal trend of any character nor it stands for the norm of the character whose distribution is under consideration.
In fact, the normal distribution is the distribution of random variable which varies continuously. The shape of the curve of the normal distribution is unimodal, symmetrical and the ends of the curve tail off asymptotically to the base. Graphically, the total are under the curve is unity.