Binomial and poisson distribution are particularly useful in the case of discrete series.The place of continuous random variables is of utmost important in statistical analysis. The normal distribution which is popularly known as normal probability distribution also, is extremely helpful in studying and analysing continuous probabilities. So many physical measurements have actual observed frequency distribution closely associated with normal distribution. English mathematician, De Moivre 1754 was the first to introduce the concept of normal distribution. Later Gauss 1777 - 1855 a well known german mathematician, physicist worked so extensively in this field tha the normal distribution became to Gaussian distribution. Quetelet 1796 - 1874, a Belgian statistician, Sir Galton 1822 - 1911 as English anthropologist, pshychologist and statistician and some other well known personalities who have contributed a lot in using this distribution in social sciences.

The real use of normal distribution in statistics can be derived from the fact that the distribution of sample means and many other statistics for large sample sizes nearly normally distributed, even if the original population is not normal. If the universe from which the sample are chosen is normally distributed the means of the samples are normally distributed around the true mean irrespective of the size of the sample.

**Condition for the Distribution to be Normal**

1. The factors affecting the events in the problem must remain independent each other.

2. The force must act in such a manner that deviations above and blow the population mean balance each other.

3. These casual forces must be very large in number and almost of weights.

4. The forces as for as possible must be uniform over the universe from sample drawn.

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