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# A Brief Note on Normal Distribution Examples

Normal distribution additionally called the Gaussian distribution, is a probability distribution that is symmetric approximately to the mean, displaying that facts close to the mean are more common in incidence than facts far from the suggested. In graph shape, the normal distribution will appear as a bell curve.

A normal distribution is a proper term for a probability bell curve. In an ordinary distribution, the mean is zero and the standard deviation is 1. They are symmetrical, however, no longer all symmetrical distributions are normal. As we know that the data is distributed in different manners. But in most cases, the data is spread across the central value with no focus on the right or left side. Then, that case is referred to as the normal distribution. In normal distribution, mean, median, and mode are equal.

## Standard Deviation

The standard deviation is defined as the measurement of how distributed the numbers are. The normal distribution is also called a Gaussian distribution.  The distribution is symmetrical about the mean- this means that half of the value will fall below the mean and above the mean. Mean and standard deviation are the different values of the normal distribution. The mean is defined in a location aspect and the standard deviation is defined in the scale aspect. The mean is defined at the peak of the curve is centred. The curve of the mean is directly affecting the mean. When the mean is increasing, the curve will move towards the right, on the other hand, when the mean is decreasing the curve will move towards the left. Stretching or squeezing of the curve will be done in the standard deviation.

## Central limit theorem-

The central limit theorem is based on the working of the normal distribution in statistics. The data is collected from different random samples which are within the population. The sampling distribution of the mean is also called the distribution of the mean. The central limit theorem includes the large numbers and the sampling distribution of the mean is normally distributed. Increasing the sample size the sample mean will move toward the population mean. Using the mean and the standard deviation in the normal distribution, the normal curve of the data can be used by the probability density function.  In the probability density function, the area under the curve describes the probability.

## Examples

1. ### Height of the population of the world

The height of the population is the instance of normal distribution. The general public in a selected population is of average height. The range of humans taller and shorter than the common height humans are almost identical, and a completely small range of humans are both extremely tall or extraordinarily short. But, height isn’t always a single feature, numerous genetic and environmental elements influence height. Therefore, it follows the normal distribution.

1. ### Rolling a dice (once or multiple times)

A truthful rolling of dice is likewise a good example of normal distribution. In a test, it has been determined that when a dice is rolled 100 times, the probability to get ‘1’ are 15-18% and if we roll the dice one thousand instances, the possibility to get ‘1’ is, once more, the same, which averages to 16.7% (1/6). If we roll two dices concurrently, there are 36 possible combos. The probability of rolling ‘1’ (with six feasible combos) again averages to around sixteen.7%, i.e., (6/36). Extra the wide variety of dices more problematic can be the normal distribution graph.

1. ### To judge the intelligent quotient level of children

In this scenario of growing competition, most mothers and fathers, in addition to youngsters, need to analyze the smart Quotient level. Nicely, the IQ of a particular populace is a normal distribution curve; in which the IQ of a majority of the people inside the population lies within the normal variety while the IQ of the rest of the population lies inside the deviated variety.

## Conclusion -:

The Normal Distribution is defined via the probability density function for a non-stop random variable in a system. Allow us to say, f(x) is the probability density function and X is the random variable. Subsequently, it defines a function that is integrated between the variety or interval (x to x + dx), giving the probability of random variable X, by considering the values between x and x+ dx. We have also discussed three examples of normal distribution in detail to understand the concept in a better way.

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## What do you mean by normal distribution?

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