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Statistics Simplified# 2: Sampling

How do we decide if it is a perfectly cooked rice?

We randomly pick one grain of rice and check. Based on our findings on the single grain, we infer that the entire rice bowl is perfectly cooked or not.

Sampling is a process of understanding the behavior of the entire group by learning the behavior of a portion of the group.

The single grain of rice in the above example was a sample, and the process of picking the grain is known as Sampling.

Why do we sample?

The primary reason is that it is easy to collect data for a sample than the entire population.

Data collected using various sampling techniques are practical, economical, handy, and adaptable.

Types of Sampling Techniques

There are two popular sampling methods:

  • Probability Sampling

  • Non-Probability Sampling.

In probability sampling, every member of the population has a chance of getting selected as a sample. We use this technique when we want our sample to be a representation of the entire population. We use this for quantitative analysis.

In the non-probability sample, samples are selected based on specific criteria. Not every individual has a chance of being selected. This technique is applicable in research and qualitative analysis. It helps to get a basic understanding of a small group or population under a specific study.

This article explains the popular methods used in probability sampling.

Simple Random Sample

We randomly pick samples from the entire population. Every member has an equal opportunity of getting selected.

For example, for conducting an employee-based survey, we randomly picked employees within an organization.

Systematic Sample

We randomly pick the first sample, and then after that, we choose every nth item in the data.

In the example below, we pick every fourth element from the sample after choosing the second item.

We arranged the employees by the employee ID for the same survey and randomly picked an employee (Emp ID 2). Then we select every 4th employee after that (Emp ID 6, 10, 14…).

Stratified Sample

Strata mean layers. We pick members for Sampling from each layer. For example, we have four groups, and we ensure we select at least one from each group.

This time for the survey, we arranged data based on employee’s designation (Associate, SME, TL, Manager…). Then we randomly pick employees from each designation group.

Cluster Sample

We divide the population into different clusters with similar characteristics. Then, we randomly pick the entire group.

For conducting the survey, we are picking all the employees within a department or team.


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