Q: Why normal distribution is better than other distributions?

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The Normal ditribution is symmetric but so are other distributions.

we prefer normal distribution over other distribution in statistics because most of the data around us is continuous. So, for continuous data normal distribution is used.

The normal distribution has two parameters, the mean and the standard deviation Once we know these parameters, we know everything we need to know about a particular normal distribution. This is a very nice feature for a distribution to have. Also, the mean, median and mode are all the same in the normal distribution. Also, the normal distribution is important in the central limit theorem. These and many other facts make the normal distribution a nice distribution to have in statistics.

No. There are many other distributions, including discrete ones, that are symmetrical.

You cannot. There are hundreds of different distributions. The shapes of the distributions depend on their parameters so that the same distribution can be symmetric when the parameters have some specific value, but is highly skewed - in either direction - for other values.

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Why we prefer Normal Distribution over the other distributions in Statistics

The Normal ditribution is symmetric but so are other distributions.

The normal distribution has two parameters, the mean and the standard deviation Once we know these parameters, we know everything we need to know about a particular normal distribution. This is a very nice feature for a distribution to have. Also, the mean, median and mode are all the same in the normal distribution. Also, the normal distribution is important in the central limit theorem. These and many other facts make the normal distribution a nice distribution to have in statistics.

we prefer normal distribution over other distribution in statistics because most of the data around us is continuous. So, for continuous data normal distribution is used.

Gaussian distribution. Some people refer to the normal distribution as a "bell shaped" curve, but this should be avoided, as there are other bell shaped symmetrical curves which are not normal distributions.

The Normal distribution is a probability distribution of the exponential family. It is a symmetric distribution which is defined by just two parameters: its mean and variance (or standard deviation. It is one of the most commonly occurring distributions for continuous variables. Also, under suitable conditions, other distributions can be approximated by the Normal. Unfortunately, these approximations are often used even if the required conditions are not met!

Only one. A normal, or Gaussian distribution is completely defined by its mean and variance. The standard normal has mean = 0 and variance = 1. There is no other parameter, so no other source of variability.

No. There are many other distributions, including discrete ones, that are symmetrical.

The Normal distribution is, by definition, symmetric. There is no other kind of Normal distribution, so the adjective is not used.

You cannot. There are hundreds of different distributions. The shapes of the distributions depend on their parameters so that the same distribution can be symmetric when the parameters have some specific value, but is highly skewed - in either direction - for other values.

For theoretical reasons (such as the central limit theorem), any variable that is the sum of a large number of independent factors is likely to be normally distributed. For this reason, the normal distribution is used throughout statistics, natural science, and social science as a simple model for complex phenomena.

It has no special name - other than a normal (or Gaussian) distribution graph.