How to Determine Sample Size

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  • 0:05 Sample Size
  • 1:07 Ideal Sample Size
  • 3:55 Real-World Issues
  • 5:35 Lesson Summary
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Lesson Transcript
Instructor: Natalie Boyd

Natalie is a teacher and holds an MA in English Education and is in progress on her PhD in psychology.

How many subjects should a researcher use in his or her experiment? In this lesson, we'll examine the elements that go into sample size, including how to figure out how big a sample should be and what real-world issues influence sample size.

Sample Size

Imagine that you are a psychologist. You want to do a study on whether drinking coffee helps people be more productive at work. That should be pretty easy to test, right? All you have to do is check how productive workers are before they drink coffee, and then give them coffee and test how productive they are afterward.

Even a seemingly simple study like this has many components to it. One major issue that has to be addressed near the beginning of planning a study is that of sample size. The sample of a study is the subjects included in the study.

For example, who should you give the coffee to? 'Every worker in America isn't exactly a practical answer. You could do the study on workers at a certain company or on workers in several companies. You could include only workers in a certain department or those holding a specific job.

As you can tell, there are a lot of decisions when choosing a sample. One of the biggest decisions is how many subjects to include in the sample. Let's take a closer look at how to determine the best sample size for a study.

Ideal Sample Size

There are many things that can dictate the size of your sample. Let's start by figuring out the ideal sample size, the one that you would have if you lived in a perfect world. Then, we'll look at how real-world issues can play a role in determining what that sample size actually ends up being.

In general, a larger sample size is better. Why is this? Well, all research is interested in making inferences about the population at large. The larger the sample size, the closer you are to having everyone in the population in your study.

For example, what would happen if you decided to do your coffee study on just three people? Maybe one of them is taking a drug that interacts with caffeine. As a result, when this person drinks coffee, they don't really get any more energetic or productive.

In your study, one-third of the sample has no reaction to coffee due to that drug. But in the actual population, maybe only two or three percent of people take this drug. Your study makes it look like a lot of people don't react to coffee because of the drug. Your results are not accurate.

Inaccuracy due to a difference in the sample and the population is called error in research. A larger sample size reduces error. If, for example, you increased your sample size from three people to three hundred people, it is less likely that one-third of your sample will be taking the drug that makes them less sensitive to caffeine.

In addition to the general rule that 'bigger is better' when it comes to sample size, the way you choose your sample can also play a factor in how large your ideal sample size is. Choosing subjects for your sample in a random manner (known as probability sampling) means that you need fewer subjects than choosing them in a non-random manner (known as non-probability sampling).

This is because non-probability sampling leads to more error than probability sampling. Think about it like this: if you randomly choose your sample, the chances of you choosing someone who either doesn't react at all to coffee or is very sensitive to coffee are close to that of the population at large. In other words, because each person in the population has an equal chance of being picked in a probability sample, you have a better chance of picking a sample that represents the population.

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