Statistics do not convey emotion. They shock us for a minute or two, and then we click again. by Madeleine M. Kunin, American diplomat | Author | Politician.

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Source: idatassist.com


What is Sampling?

Suppose you work in a IT company and your boss asked you to collect the data from the social networks and from various platforms. Sure you can extract the data from the APIs and from other techniques but the question raised here how you handle all that tons of data. 

In current time, computer memory is not that expensive but still their is a limitation if the size of data is enormously large. If you have great deal of money sure you can invest in computational power and memory to process that data but if there is a technique or a way that can help you to analyze that data on your normal system then what's the problem? your work will become a lot easier. You will save money and time!

In simple words, if you have your hands on a large dataset and you have to perform some analysis or modeling, it might give you "out of memory"/"out of bound" message on your screen. So, you pick up a subset of data that could represent your original population in order to make some quick and reliable decisions.

Why do we need Sampling?

Now we understood the basic concept behind sampling but the real question here is that why do we need it in real life or work?

Well for starters, it enables us to determine the population's characteristics by only observing a subset of whole population.

1. Selecting a subset requires less time than selecting every item in a population.
2. Cost-Efficient method. You save your precious time and money.
3. Analysis of subset/sample comes in more structured format than of original population.

Steps to perform Sampling


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Common Sampling Types

Probability Sampling

It uses randomization to select sample elements that means every element has equal chance to get selected.
 1. Simple Random Sampling
 2. Stratified Sampling
 3. Systematic Sampling
 4. Cluster Sampling

Non-Probability Sampling

It uses non-random technique to select elements that means all elements do not have equal chance to get selected.
 1. Quota Sampling
 2. Snowball Sampling
 3. Judgement Sampling
 4. Convenience Sampling


In later Sampling posts, I will be sharing Sampling types in deep with their implementation!

Stratified Sampling 

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