**Sponsored Ad:**

Transform your **financial **life by **tracking**, **planning**, and **optimizing **your budget effortlessly with our user-friendly **Google Sheets Budget Planner Template**! **I Want This Template**

Standard deviation is a statistical measure that quantifies the amount of variation or dispersion in a set of data values.

In simpler terms, it tells you how spread out the numbers in a data set are around the mean (average).

**The formula for standard deviation is:**

Where:

*σ*is the standard deviation.*x**i* represents each value in the dataset.*μ*is the mean (average) of the dataset.*N*is the total number of values in the dataset.- ∑∑ denotes the sum of all the squared differences.

This formula calculates the square root of the average of the squared differences from the mean, providing a measure of the spread of the values around the mean.

## How to Calculate Standard Deviation

Let’s break down the process of calculating standard deviation into simpler, more digestible steps.

Think of standard deviation as a measure that tells us how spread out the numbers in a set of data are.

### Understanding the Concept

**What is Standard Deviation?**

Imagine you and your friends take a quiz. Standard deviation will help you understand how varied your scores are.

If everyone scored similarly, the standard deviation would be low.

However, if the scores are all over the place, the standard deviation will be high.

**Why is it Important?**

It gives you an idea of how “spread out” the data is. A smaller standard deviation means the data points are closer to the mean (average), and a larger one means they are more spread out.

### Steps to Calculate Standard Deviation

**Collect Your Data**

Let’s say you have a set of numbers. These could be anything: scores on a test, heights of people, etc.

**Find the Mean (Average)**

Add up all the numbers.

Divide by how many numbers there are.

This gives you the average or the mean.

**Subtract the Mean and Square the Result for Each Number**

Take each number in your data set.

Subtract the mean from that number (this could be negative, but that’s okay).

Square this result (multiply it by itself). Squaring makes sure all the results are positive.

**Find the Mean of These Squared Differences**

Add up all the squared differences.

Divide by the number of data points.

This gives you the mean of these squared differences.

**Take the Square Root of That Mean**

This final step is to take the square root of the mean you just found.

The number you get is the standard deviation.

### Example:

Imagine these are quiz scores: 2, 4, 4, 4, 5, 5, 7, 9.

**Mean**: Add them up (35) and divide by how many there are (8). So, the mean is 4.375.

**Subtract Mean and Square**:

(2 – 4.375)² = 5.64

(4 – 4.375)² = 0.14, and so on for each number.

**Mean of Squared Differences**: Add all these up (20.25) and divide by 8. You get 2.53125.

**Square Root**: The square root of 2.53125 is about 1.59.

This 1.59 is the standard deviation. It tells you that, on average, each quiz score was about 1.59 points away from the average score of 4.375.

### Visual Aid

Think of a target with arrows shot at it. If the arrows are close to the bullseye (mean), the standard deviation is small. If they’re spread out, it’s large.

Or, picture a flock of birds. Are they flying closely together (low standard deviation) or spread out across the sky (high standard deviation)?