population.

Sampling at your convenience

If you have read this chapter from the beginning until now, you may be feeling a little exasperated. And

that may be because all the sampling strategies we have discussed so far — SRS, stratified sampling,

systematic sampling, and cluster sampling — involve a lot of work for the researcher. In an SRS, you

need to have a list of the population from which to draw, and in stratified sampling, you have to know

the value of the characteristics on which you want to stratify your sample. Each of these features makes

designing your sampling frame more complicated.

Thinking this way, both systematic sampling and cluster sampling also add complexity to your

sampling frame. In systematic sampling, whether you use a static list or you sample in real time, you

need to keep track of the details of your sampling process. In cluster sampling, you may be using a map

or system of groupings from which to sample, and that also involves a lot of recordkeeping. You may

be asking by now, “Isn’t there an easier way?”

Yes! There is an easier and more convenient way: convenience sampling. Convenience sampling is

what you probably think it is — taking a sample from a population based on convenience. For

example, when statistics professors want to know what students think about a new policy on campus,

they can just ask whoever is in their classes, as those students are a convenient sample of the student

population.

The problem is that the answer they get may be very biased. Most of the students in their classes may

come from the sciences, and those studying art or literature may feel very differently about the same

policy. Although our convenience sample would be a valid sample of the background population of

students, it would be such a biased sample that the results would probably be rejected by the rest of

the faculty — especially those from the art and literature departments!

Given that the results from convenience samples are usually biased, you may think that

convenience sampling is not a good strategy. In actuality, convenience sampling comes in handy if

you have a relatively low-stakes research question. Customer satisfaction surveys are usually

done with convenience samples, such as those placing an order on a restaurant’s app. It is simple

to program such a survey into an app, and if the food quality is terrific and the service terrible, it

will be immediately evident even from a small convenience sample of app users completing the

survey.

While low-stakes situations are fine for convenience sampling, high-stakes situations — like

studying whether a new drug is safe and/or effective — require study designs and sampling

approaches completely focused on minimizing bias. As with SRS, convenience sampling is prone

to omitting important subgroups from the sample. Minimizing bias through sampling and other

strategies is covered in detail in Chapter 5, which examines clinical research and describes how

researchers must present a well-defined protocol that includes selection criteria, a sampling plan,

and an analytic plan that undergoes regulatory approval prior to the commencement of research