The normal distribution is widely used in understanding distributions of factors in the population. Because the normal distribution approximates many natural phenomena so well, it has developed into a standard of reference for many probability problems.

Normal/Gaussian Distribution is a bell-shaped graph that encompasses two basic terms- mean and standard deviation. It is a symmetrical arrangement of a data set in which most values cluster in the mean and the rest taper off symmetrically towards either extreme. Numerous genetic and environmental factors influence the trait.

**Central Limit Theorem**

Normal distribution follows the central limit theory which states that various independent factors influence a particular trait. When these all independent factors contribute to a phenomenon, their normalized sum tends to result in a Gaussian distribution.

**Normal Curve**

The mean of the distribution determines the location of the center of the graph, the standard deviation determines the height and width of the graph and the total area under the normal curve is equal to 1.

Let’s understand the daily life examples of Normal Distribution.

**1. Height**

The height of people is an example of normal distribution. Most of the people in a specific population are of average height. The number of people taller and shorter than the average height people is almost equal, and a very small number of people are either extremely tall or extremely short. Several genetic and environmental factors influence height. Therefore, it follows the normal distribution.

**2. Rolling A Dice**

A fair rolling of dice is also a good example of normal distribution. In an experiment, it has been found that when a dice is rolled 100 times, chances to get ‘1’ are 15-18% and if we roll the dice 1000 times, the chances to get ‘1’ is, again, the same, which averages to 16.7% (1/6). If we roll two dice simultaneously, there are 36 possible combinations. The probability of rolling ‘1’ (with six possible combinations) again averages to around 16.7%, i.e., (6/36). More the number of dice more elaborate will be the normal distribution graph.

**3. Tossing A Coin**

Flipping a coin is one of the oldest methods for settling disputes. We all have flipped a coin before a match or game. The perceived fairness in flipping a coin lies in the fact that it has equal chances to come up with either result. The chances of getting a head are 1/2, and the same is for tails. When we add both, it equals one. If we toss coins multiple times, the sum of the probability of getting heads and tails will always remain 1.

**4. IQ**

In this scenario of increasing competition, most parents, as well as children, want to analyze the Intelligent Quotient level. Well, the IQ of a particular population is a normal distribution curve; where the IQ of a majority of the people in the population lies in the normal range whereas the IQ of the rest of the population lives in the deviated range.

**5. Technical Stock Market**

Most of us have heard about the rise and fall in the prices of shares in the stock market. These changes in the log values** **of Forex rates, price indices, and stock prices return often form a bell-shaped curve. For stock returns, the standard deviation is often called volatility. If returns are normally distributed, more than 99 percent of the returns are expected to fall within the deviations of the mean value. Such characteristics of the bell-shaped normal distribution allow analysts and investors to make statistical inferences about the expected return and risk of stocks.

**6. Blood Pressure**

Blood pressure generally follows a Gaussian distribution (normal) in the general population, and it makes Gaussian mixture models a suitable candidate for modelling blood pressure behaviour.

**7. Shoe Size**

Have you wondered what would have happened if the glass slipper left by Cinderella at the prince’s house fitted another woman’s feet? He would have ended up marrying another woman. It has been one of the most amusing assumptions we all have ever come across. As per the data collected in the US, female shoe sales by size are normally distributed because the physical makeup of most women is almost the same.

**8. Birth Weight**

The normal birth weight of a newborn ranges from 2.5 to 3.5 kg. The majority of newborns have normal birthweight whereas only a few percent of newborns have a weight higher or lower than normal. Hence, birth weight also follows the normal distribution curve.

**9. Student’s Average Report**

Nowadays, schools are advertising their performances on social media and TV. They present the average result of their school and allure parents to get their children enrolled in that school. School authorities find the average academic performance of all the students, and in most cases, it follows the normal distribution curve. The number of average intelligent students is higher than most other students.

flipping a coin can be the example of binomial distribution as it has two possible outcomes characterized as success and failure.

???

yes its a bernaulie dist and not binomial as it has only 2 outcomes . either 1 or 0 .

Things like shoe size and rolling a dice aren’t normal – they’re discrete!

Discrete data can also be modelled by normal distribution when we make continuity correction

I guess these are not strictly Normal distributions, as the value of the random variable should be from -inf to +inf.

These are bell-shaped distributions.

Standard deviation in IQ scores is less for other races. 12 for bot black and asians. Little wonder since they all seem to group in appearance too. Even if races had equal iq score averages the smaller SD is a killer. With an SD of 15 you get an iq of 130 1 in 44,

With an SD of 12 you get an iq of 130 1 in 161. That is with 100 average. Its bad for blacks who have a much smaller average but it also wipes out the advantages of a slightly higher North Asian mean of 105 pretty quickly. Its good to be white.

Thanks man wow 😳 very breathtaking