# Statistics Basics — Population vs Sample and Descriptive vs Inferential Statistics

## Topics Covered

- What is Statistics?
- Population vs Sample
- Descriptive Statistics
- Inferential Statistics

**Difficulty Level:** Easy

# Statistics

Google defines statistics as a discipline that concerns itself with the collection, organization, analysis, interpretation and presentation of data. Yes, that is pretty much it!

**You have some data…**

*You organize it to make it easier to interpret, **THAT’S STATISTICS!*

*You look at the data and figure out something the traces of numbers told you a story, **THAT’S STATISTICS!*

*Your data made you understand something and you interpret it in your own way, **THAT’S STATISTICS!*

*You present the data in such way that — **OKAY OKAY, YOU GOT THE POINT!!!*

# Population Vs. Sample

Somebody said “An example is worth a thousand words.” Wait, did they really say that? Well, somebody must have. Let’s understand it with the help of an example.

Let’s say you want to find out what genre of movie people of Kathmandu mostly like. How can you do that?

Well, you can visit each and every house in Kathmandu and ask everyone what they like and analyze the response. Who would do such a thing? That is very tedious and might be near to impossible.

So, if not that, what? You can ask a small group of people from different places in Kathmandu and then do some calculations and find out what majority people ** might** like.

There you have it. On the first tedious scenario, you collected data from a population (because **EVERYONE!**) and in the second scenario, you collected data from a subset (a small group of people), you collected data from a sample.

# Descriptive Statistics

This is where…

You analyze data, summarize data, organize data in the form of Numbers and Graphs. The definitions of statistics I gave you falls under Descriptive Statistics.

You analyze data by making pie charts, histograms, bar graphs, etc.

You find out mean, medium and mode.

You calculate the variance (Range, Variance and Standard Deviation).

THIS IS APPLICABLE FOR BOTH SAMPLE AS WELL AS POPULATION.

# Inferential Statistics

Oxford definition of Inference: a conclusion reached on the basis of evidence and reasoning.

This is where…

You use **SAMPLE DATA** to draw a conclusion **FOR THE POPULATION.**

Example: You can say things like: Hmm, 39 people out of 50 people like the movie Pashupati prasad in the sample. That means, 78% percentage of the **total population** must also like the same movie.

You can also calculate probability to find out how correct the predictions are!

Things like **Confidence Intervals** and **Margins of Errors** also falI under this type of statistics. Woah. heavy words?

Okay, an example again. If you can say things like: I am 98% certain that 78% percent people like Pashupati Prasad, that’s confidence interval in general.

And if you say things like: 78% percent people do like Pashupati Prasad by a ± 5% error margin, you mean there might be a slight error of 5% as the data is taken from a sample and not a population.

# YOU DID IT!

**CONGRATULATIONS, YOU LEARNT SOMETHING NEW TODAY. I AM SO PROUD OF YOU! :)**

Want to help me with Gurudakshina ;) ?

Well, there is no patreon service in Nepal but I will throw in my esewa mail: *roshan.parajuly1@gmail.com* :)

References:

The Organic Chemistry Tutor (YouTube)

Krish Naik (YouTube)

For more such blogs: blogs.roshanparajuli.com.np