# Standard Normal Distribution Tutorial

**Standard Normal Distribution Tutorial**, In this tutorial one can learn about the importance of normal distribution in data science. We Prwatech the Pioneers of Data Science Training Sharing information about normal distribution to those tech enthusiasts who wanted to explore the Data Science and who wanted to become the Data analyst expert. Are you the one who is looking for the best platform which provides information about normal distribution probability in Data Science? Or the one who is looking forward to taking the advanced Course from India’s Leading Data Science training institute? Then you’ve landed on the Right Path.

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**Standard Normal Distribution Definition**

Data can be Distributed in different ways, i..e.

It can be distributed from Left to Right.

It can be distributed from Right to Left.

It can be an irregular structure of distribution i.e. Sometimes Up and Sometimes Down.

It can be Evenly Distributed making a bell curve.

This even distribution of data making bell-like the curve is known as **Normal Distribution.**

**Standard Normal Distribution** can be described as the Distribution of data from a collection of **Continuous Random variables** forming a **Bell-like** Shape having a Symmetric Structure.

In Normal Distribution

Mean=Mode=Median** **

The standard normal distribution has two parameters:

- µ = the mean
- σ = standard deviation.

The normal distribution is a proper term for a probability bell curve.

Normal distribution is symmetrical distribution, but not all symmetrical

distributions are normal.

In reality, most pricing distributions are not perfectly normal.

**Why we need Standard Normal Distribution?**

The Standard Normal Distribution is most likely used for all kinds of Distribution’s because it approximates in many natural phenomena’s that it has become a standard reference to many probability problems

Many things actually are normally distributed, or very close to it. For example, height and intelligence are approximately normally distributed; measurement errors also often have a normal distribution

The normal distribution is easy to work with mathematically. In many practical cases, the methods developed using normal theory work quite well even when the distribution is not normal.

There is a very strong connection between the size of a sample N and the extent to which a sampling distribution approaches the normal form. Many sampling distributions based on large N can be approximated by the normal distribution even though the population distribution itself is definitely not normal.

**How to Calculate Standard Normal Distribution in Data Science?**

The average number of acres burned by forest and range fires in a large New Mexico county is 4,300 acres per year, with a standard deviation of 750 acres. The distribution of the number of acres burned is normal.

What is the probability that between 2,500 and 4,200 acres will be burned in any given year?

**Standard Normal Distribution Formula with Examples**

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