What do you call when each member of the given population has an equal chance of being selected?

The secret to minimizing biased data!

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Introduction

“Why should I care about random sampling?”

Here’s why you should know about random sampling.

If you’re a data scientist and want to develop models, you need data.

And if you need data, SOMEONE needs to collect data.

And if someone is collecting data, they need to make sure that it is not biased or it will be extremely costly in the long run.

Therefore, if you want to collect unbiased data, then you need to know about random sampling!

What exactly is random sampling?

Random sampling simply describes when every element in a population has an equal chance of being chosen for the sample.

Sounds simple right? Unfortunately, it’s a lot easier said than done. This is because there are a lot of logistics that need to be considered in order to minimize the amount of bias.

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Random Sampling Techniques

There are 4 types of random sampling techniques:

1. Simple Random Sampling

Simple random sampling requires using randomly generated numbers to choose a sample. More specifically, it initially requires a sampling frame, a list or database of all members of a population. You can then randomly generate a number for each element, using Excel for example, and take the first n samples that you require.

To give an example, imagine the table on the right was your sampling frame. Using a software like Excel, you can then generate random numbers for each element in the sampling frame. If you need a sample size of 3, then you would take the samples with the random numbers from 1 to 3.

2. Stratified Random Sampling

Stratified random sampling starts off by dividing a population into groups with similar attributes. Then a random sample is taken from each group.

This method is used to ensure that different segments in a population are equally represented. To give an example, imagine a survey is conducted at a school to determine overall satisfaction. It might make sense here to use stratified random sampling to equally represent the opinions of students in each department.

3. Cluster Random Sampling

Cluster sampling starts by dividing a population into groups, or clusters. What makes this different that stratified sampling is that each cluster must be representative of the population. Then, you randomly selecting entire clusters to sample.

For example, if an elementary school had five different grade eight classes, cluster random sampling might be used and only one class would be chosen as a sample, for example.

4. Systematic Random Sampling

Systematic random sampling is a very common technique in which you sample every k’th element. For example, if you were conducting surveys at a mall, you might survey every 100th person that walks in, for example.

If you have a sampling frame then you would divide the size of the frame, N, by the desired sample size, n, to get the index number, k. You would then choose every k’th element in the frame to create your sample.

Using the same example, if we wanted a desired sample size of 2 this time, then we would take every 3rd row in the sampling frame.

Thanks for Reading!

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If you made it to the end, you should now have an understanding of what random sampling is and several techniques that are commonly used to conduct it. This is extremely important to minimize bias, and thus, create better models.

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Terence Shin

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