CHAPTER 6 Taking All Kinds of Samples 79

If you omit population members from your sampling frame, you get undercover-

age, which is a form of non-sampling error (the type of error you want to avoid).

Also, if you accidentally include members in your sampling frame who are not

part of the population (such as patients who moved away from the clinic after you

printed your list), and they actually get sampled, you have another form of non-

sampling error. Non-sampling error can also creep in from making sloppy mea-

surements during data collection, or making poor choices when designing your

study. Chapter 8 provides guidance on how to minimize errors during data collec-

tion, and Chapters 5 and 7 provide advice on study design.

Another sampling-related vocabulary word is simulation. When talking about

sampling, a simulation refers to pretending to have data from an entire popula-

tion from which you can take samples, and then taking different samples to see

what happens when you analyze the data. That way, you can make sample statis-

tics while peeking at what the population parameters actually are behind the

scenes to see how they behave together.

One simulation you could do to illustrate sampling error in Microsoft Excel is to

create a column of 100 values that represent ages of imaginary patients at a clinic

as an entire population.»

» ^{If you calculated the mean of these 100 values, you would be doing a simula-}

tion of the population parameter.»

» ^{If you randomly sampled 20 of these values and calculated the mean, you}

would be doing a simulation of a sample statistic.»

» ^{If you compared your parameter to the statistic to see how close they were to}

each other, you would be doing a simulation of sampling error.

So far we’ve reviewed several concepts related to the act of sampling. However, we

haven’t yet examined different sampling strategies. It matters how you go about

taking a sample from a population; some approaches provide a sample that is

more representative of the population than other approaches. In the next section,

we consider and compare several different sampling strategies.

Sampling for Success

As mentioned earlier, the purpose of taking measurements from a sample of a

population is so that you can use it to perform inferential statistics, which enables

you to make estimates about the population without having to measure the entire

population. Theoretically, you want the statistics from your sample to be as close

as possible to the population parameters you are trying to estimate. To increase