## Sample variance
**Sample variance** is a statistical measure that quantifies the spread or dispersion of a set of data points in a dataset from their mean value.   
We can calculate it using the following **formula**:

>Note
>
>In this context, **sample size** and **sample mean** refer to characteristics calculated based on the existing dataset.

You should keep in mind a **fundamental empirical rule**: the greater the sample variance, the more spread out the data is.

### How to calculate sample variance in Python? 
In **NumPy** you need to put the sequence of values (in our case, the column of the dataset) into the function `np.var()` like `np.var(df['salary'])` to calculate sample variance.

## Standard deviation
This value is similar to the variance because **standard deviation** is a square root of the variance.    
We can calculate it using `np.std()` function using NumPy library.

>Note
>
>All characteristics of datasets are considered in <a 
href="https://codefinity.com/courses/v2/ec6e4978-d3b4-4612-b2be-894504d0f970"
target="_blank"
style="color: #ff8a00; font-weight: 600; text-decoration: none;"
onmouseover="this.style.color='#ff0000'"
onmouseout="this.style.color='#d27204'">Probability Theory Mastering</a> course in more detail! In this course there is also explored the connection between the concepts of probability theory and the statistical properties of the data, understanding how these concepts are interrelated.


We will start our way of learning probability theory by considering some basic definitions and rules: what is a stochastic experiment and random event, what is independence and incompatibility of events in the context of probability theory, what is the probability and how can we calculate probabilities of different elementary events. 

In real-life tasks, we often have to deal with complex relationships and, as a result, calculate probabilities of several events or events that depend on each other. Let's consider how we can do this using probability theory.

To solve many real problems in probability theory, special models have been created that describe a particular situation. Let's consider some of the most used models that can be used to describe some discrete results of stochastic experiments.

What if the result of a stochastic experiment cannot be described by a discrete value? For this, models that work with continuous values are used. Consider the most popular of these models.

Often we are faced with the task of checking the dependence of the results of different stochastic experiments on each other. Moreover, it is necessary not only to assess the presence of dependencies but also to somehow quantify the degree of dependencies. To solve these problems, we can use covariance and correlation.

In this section, we will explore fundamental statistical concepts. We will focus on descriptive statistics, including measures of central tendency (such as mean, median, and mode) and measures of dispersion (like standard deviation). These concepts are essential for understanding the inherent characteristics of data.

Probability Theory Basics

Sample Variance and Standard Deviation

Sample variance

How to calculate sample variance in Python?

Standard deviation