Descriptive Statistics Part 4

Photo by Luke Chesser on Unsplash

Now , In this tutorial we will learn about distribution or distribution of the continuous data.

There is some of distribution that describe the continuous data.

1- Symmetric distribution

2- Skew distribution

3- Center of distribution

4- Location of distribution

5- Spread of distribution

6- Normal distribution

7- Binomial distribution


Distribution is useful to know in depth about continous data or analysis and describe the data very precisely.


So, now in this above table . We will check the distribution of the salary column.

As we know salary column is continuous data elements.

For analysis the salary tables in depth we use distribution. how salary is distributed.

And we will check this salary column come in which distribution.

1- Symmetric Distribution

A symmetric distribution is one where the left and right hand sides of the distribution are roughly equally balanced around the mean. This histogram below shows a typically symmetric distribution.

Symmetric Distribution

For symmetric distribution, the mean is approximately equal to the median. for a symmetric distribution the left and right tails are equally balanced meaning that they have about the same length.

This figure below shows the box & whisker diagram for a typically symmetric data set.

Box plot

It’s median lie in the middle of the first and third quartile.

Along the histogram, box plot is another way of representing continuous data elements.

2- Right Skewed distribution

A distribution that is skewed right (also known as positively skewed ) is shown below.

Skew distribution

Now , the picture above is not symmetric around the mean anymore. for a right skewed distribution the mean is typically greater than the median . also noticed that the tail of the distribution on the right hand is longer than the left hand size.

Box plot of skew distribution

From the box and whisker diagram we can also see that the median is closer to the first quartile than the third quartile. The fact that the right hand side tail of the distribution is longer than the left.

3- Left skew distribution

It’s completely opposite of right skew distribution.

After learnt all about distribution and different type of distribution.

So ,time to analysis some distribution examples —

examples of distribution

Q- Let us know above distribution is what kind of distribution?

Ans — Type in comment box.

Now, take example for analysis the distribution of the data without draw any diagram

Giving the Senario,

Suppose we take a sample record income of individuals or we recorded the height of an adult population as percentages.

So, it’s good to think about what shape would you except for the distribution of these variable before collecting them, so think and what shape you will except that.

4- Binomial distribution-

We saw many distribution above . they all used in continuous data , but binomial distribution use in Discrete data, like how much no of success or failure . it’s discrete 0 or 1. so , we use binomial distribution.

binomial Distribution

Note — If Discrete data is large so, binomial distribution is same as normal distribution.

Some Important points-

  • Describe the shape of a distribution aside from describing the shape of the distribution we also think about some measure of location as well as dispersion or variability.
  • First think about center where the center of the distribution so , see all diagram before we draw center is defined there.

Center can be — Mean , Median and other measurement.

  • Measure of location — Q1, Q2, median , lower , upper , variability .( for measure of location we use box plot).
  • Measure of spread — we will discuss later chapters.

If any doubt regarding it. please comment below and ask feel free.

If any doubt regarding this tutorial ask feel free on LinkedIn-

GitHub workspace link-




Data Science , Machine Learning , BlockChain Developer

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

Business Process Management Meets Data Science

Gradient Descent Intuition It is an algorithm that updates a machine learning model’s parameters…

Exploring Data: Analysis of Genshin Impact

Lies, Damn Lies, and Statistics

Algorithmic pricing, part II: AI and pricing strategy

How Data Scientists can help Government Agencies Effectively Respond to Natural Disasters

Frequency, Contingency, and Scatterplots Review

Customer Segmentation

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Puneet Singh

Puneet Singh

Data Science , Machine Learning , BlockChain Developer

More from Medium

Missing Values Identifications in Tabular Data

Data Clustering with K means for beginners.

An Introduction To Percentiles In Statistics In Just 5 Minutes