In the previous section, Random Sampling, you learned that some sampling methods were biased, and can produced samples unrepresentative of the population from which they are selected. In the next activity, you will need to be able to distinguish between a sample and a population and identify different sampling methods. Prepare for that now by formally defining the population and sample, then learning about four common sampling methods.

Sampling Methods

In a moment, you’ll learn about four sampling methods commonly used in research. It is important to note that not all statistical questions can be investigated using purely unbiased methods.  Therefore, acknowledging bias in the sampling method is crucial to interpreting the analysis. Before we introduce these methods, let’s formally define what we mean by populations and samples.

Population vs. Sample

In the previous section, Random Sampling, you identified populations and parameters from descriptions of studies. The population was the entire group of interest and the parameter was a numerical summary describing a characteristic of that group.

In real life, it is usually hard to collect data on an entire population. Therefore, samples are used in research studies to collect data to make inferences about a population. Collecting data from a sample is less time consuming and less costly than collecting data from an entire population.

Answer Questions 1 and 2 now to define the terms population and sample. 

Sampling Methods

There are four common sampling methods used in research. These sampling methods include: simple random sampling, systematic sampling, stratified sampling, and convenience sampling.

Simple Random Sampling: In simple random sampling, every sample of a given size has the same chance of being selected; this results in every individual of the population having an equal chance of being selected. Recall that random number generators are used to select samples. In the following figure, the random number generator selected numbers 24, 22, 27, 25, and 13, resulting in the highlighted individuals being selected for the sample.

[See the original DC page for how the images below need to convey the information]

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Systematic Sampling: In systematic sampling, every individual in the population is given a number and individuals are chosen at regular intervals, with a random starting point (usually among the first several). The following figure illustrates a systematic sample where every 4th individual is selected, starting at the 3rd individual (starting point selected at random). The individuals selected for the sample are highlighted.

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Stratified Sampling: In stratified sampling, a population is divided into two or more groups (called strata) according to some criterion (i.e., geographic location, grade level, age group, income group, etc.), and a sample is selected from each strata using simple random sampling or systematic sampling.

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Convenience Sampling: A convenience sample is a sample of individuals who are most accessible to the researcher. A convenience sample is usually not random or representative of the population. This is an example of a biased sampling method because it has a tendency to produce samples that are not representative of the population. For example, you take a sample of your friends because it is easy to collect information about them.

Identifying Sampling Methods

In Questions 3–7, you’ll read a brief description of a study, then identify the sampling method used. See the video below for a demonstration of how to differentiate between the methods in a description of a study.

Let’s summarize key features of each of the four common sampling methods we discussed.

• Simple random sampling assigns a number to every member of the population then uses a random number generator to select a sample.
• Systematic sampling assigns a number to every member of the population then choses individuals from the population at regular intervals (e.g. every 4th individual from a randomly selected starting point).
• Stratified sampling divides a population into groups via some criterion then uses simple random selection or systematic selection to collect a sample from each group.
• Convenience sampling selects a sample most accessible to the researcher.

Now you try the questions below. Be sure to check the feedback to assess your understanding.

Summary

In this What to Know page, you defined the terms population and sample to understand that in statistics, data is collected on a sample drawn from a population because it is often impossible due to time and financial constraints to collect data on an entire population.  You also learned to identify four common sampling methods and saw that convenience sampling is usually a biased method. Let’s summarize where these concepts appeared in the questions for this page.

• In Questions 1 – 2, you defined sample vs. population.
• In Questions 3 – 7, you identified the 4 different common sampling methods.

Hopefully, you have a basic understanding of these ideas now. You can come back to this page any time to refresh the definitions and methods. Now it’s time to move on to Forming Connections!