Random sampling: simple random sampling

Written by Carlos Ochoa el 16 de January 2017

Continuing with our series of posts on sampling, today we'll review the first random sampling method: simple random sampling. This is one of the most popular sampling methods, and it serves as a reference for many others, even though, as we’ve said before, in practice it can be difficult to implement.


Simple random sampling (SRS) is a sampling method in which all of the elements in the population—and, consequently, all of the units in the sampling frame—have the same probability of being selected for the sample. It would be along the lines of having a fair raffle among every individual in the population: we give everyone raffle tickets with unique sequential numbers, put them all in a basket and draw numbers from the basket at random. The individuals whose numbers are selected become our sample. Obviously, in practice, these methods can be automated using computers.

Category: online sample | random sampling | sampling

Sampling: what it is and why it works

Written by Carlos Ochoa el 27 de December 2016

Whenever I look at the stats for this modest blog, I always notice the same pattern. The number of visits aligns perfectly with the Pareto principle: 20% of our posts generate 80% of our page views. Of that 20%, the majority discuss how to calculate the size of a representative sample in order to conduct an opinion poll.

Given the apparent interest in this topic, today we are launching a series of posts on sampling: what it is, different sampling methods, when it’s useful to use one method or another, and so on. We hope that this information will be useful to students, to stats enthusiasts, and to professionals whose statistical expertise is a little rusty.


Sampling is the process of selecting a group of individuals from a population in order to study them and characterize the population as a whole.

It’s a pretty simple idea. Let’s say we want to know something about a population—the percentage of people in Mexico who smoke, for example. One way to go about this would be to call up everyone in Mexico (122 million people) and ask them if they smoke. The other way would be to get a subgroup of individuals together (1,000 people, for example) and ask them if they smoke, and then use this information as an approximation of the information we really want. This group of 1,000 people who make it possible for us to understand the behavior of Mexicans in general is called a sample, and the way we select them is called sampling.

Category: online sample | random sampling | sampling

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