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# Standard Normal Distribution Table

## Introduction

The Normal Distribution, also called the Gaussian distribution, as we know is the most significant continuous probability distribution in statistics and probability. The Normal Distribution is defined by the probability density function for a continuous random variable in a system. It is used to approximate other probability distributions as well. One of the forms of the normal distribution is the standard normal distribution. What is the standard normal distribution and how is relevant in statistics? Let us find out.

## What is Standard Normal Distribution?

The standard normal distribution is one of the forms of the normal distribution. It occurs when a normal random variable has a mean equal to zero and a standard deviation equal to one. In other words, a standard normal distribution is said to occur when a distribution has a mean of 0 and a standard deviation of 1. Moreover, centred at zero, the standard normal distribution gives the degree to which a given measurement deviates from the mean. The standard normal distribution is represented by the letter Z. The standard score or a z-score of a standard normal distribution is the name given to the random variable of the distribution. Let us now learn the formula for finding the z score of a normal distribution.

## Formula for finding the z-score

Every normal random variable X can be transformed into a z score. The formula used for this purpose is –

z =  x- μ  where

x  is a normal random variable,

μ is the mean of X, and

σ is the standard deviation of X.

let us now understand the standard normal distribution table.

## How is Standard Normal Distribution Table formed?

The probability of a regularly distributed random variable Z can be defined by a standard normal distribution table. We know that a normal distribution is also known as a Gaussian distribution and is a persistent probability distribution. The purpose of creating a standard normal distribution table is for determining the region under the bend ( f ( z ) ) for discovering the probability of a specified range of distribution.  It is important to note here that since the shape of the normal distribution density function f ( z ) resembles a bell, therefore it is also known as a Bell Curve.

What does a normal distribution table reflect?

The cumulative probability linked with a particular z-score is presented in a normal distribution table. The whole number and tenth place of the z-score are represented by the rows of the standard normal distribution table. The hundredth place of the z-score is represented by the columns of the standard normal distribution table. In other words, Z-scores tell you how many standard deviations from the mean each value lies.

Let us understand it through an example.

Example

Suppose we wish to find the cumulative probability of a z-score equal to -1.21. How would we do that using a standard normal distribution table?

Solution

In order to find the cumulative probability of a z-score equal to -1.21, we will check the cross-reference the row that contains -1.2 of the table along with the column that holds  0.01. We will find that a standard normal random variable will be less than -1.21 is 0.1131. this means that P ( z ) = 0.1131 which is the z score of the table.

Let us now understand the standard normal distribution table according to various values of z.

### Standard Normal Distribution Table when Statistic is less than Z (i.e. between negative infinity and Z)

Below is the Standard Normal Distribution Table when Statistic is less than Z (i.e. between negative infinity and Z)

### Standard Normal Distribution Table when Statistic is greater than Z

Below is the Standard Normal Distribution Table when Statistic is greater than Z

Let us understand it using an example.

Example

A unit known as radar is used to measure the speeds of buses on a motorway. The speed of the buses is normally distributed with a mean of 90 km/hr and a standard deviation of 10 km/hr. What is the probability that a bus sliced randomly is travelling at more than 100 km/hr?

Solution

Let y be any random variable that indicates the speed of buses. We have been given that  Mean(μ)= 90 and standard deviation ( σ) = 10. We have to find the probability that y is higher than 100 or P(y > 100). We will find the probability through the standard normal distribution formula.

Now, we know that –

z = (X– Mean) / Standard deviation, i.e.

z =  x- μ  where

x  is a normal random variable, μ is the mean of X, and σ is the standard deviation of X.

Now, if we take x= 100 ,then  z = (100 – 90) / 10 = 1

P(y > 90) = P(z > 1) = (Total area) – (area to the left of z = 1)

= 1 – 0.8413 = 0.1587

Therefore, the probability that a bus selected randomly has a speed greater than 100 km/hr is 0.1587

## Interpreting the z-score using the standard normal distribution table

Let us now understand how to interpret the z-score using the standard normal distribution table. The following points need to be considered during interpretation of the z-score –

1. The interpretation of a z-score of less than 0 is that an element is less than the mean.
2. The interpretation of a z-score of greater than 0 is that an element greater than the mean.
3. The interpretation of a z-score of equal to 0 is that an element is equal to the mean.
4. The interpretation of a z-score of equal to 1 is that an element is 1 standard deviation greater than the mean.
5. The interpretation of a z-score of equal to 2 is that an element is 2 standard deviations greater than the mean.

## Key Facts and Summary

1. The standard normal distribution is one of the forms of the normal distribution.
2. A normal distribution is also known as a Gaussian distribution and is a persistent probability distribution.
3. A standard normal distribution is said to occur when a distribution has a mean of 0 and a standard deviation of 1.
4. Every normal random variable X can be transformed into a z score. The formula used for this purpose is –  z =  x- μ  where x  is a normal random variable, μ is the mean of X, and σ is the standard deviation of X.
5. Since the shape of the normal distribution density function f ( z ) resembles a bell, therefore it is also known as a Bell Curve.
6. The interpretation of a z-score of less than 0 is that an element is less than the mean.
7. The interpretation of a z-score of greater than 0 is that an element greater than the mean.
8. The interpretation of a z-score of equal to 0 is that an element is equal to the mean.
9. The interpretation of a z-score of equal to 1 is that an element is 1 standard deviation greater than the mean.
10. The interpretation of a z-score of equal to 2 is that an element is 2 standard deviations greater than the mean.

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