A random sample is a very important component in research. In this lesson, learn more about random samples, some advantages and disadvantages of each, and look at some examples.
What is a Random Sample?
A random sample is a sample in which each member of the population has an equal chance of being selected to represent the whole.
In order for us to truly understand what a random sample is, we must first distinguish between a sample and a population. A population is all members of a defined group that have certain characteristics or attributes that we are interested in studying. Suppose we were interested in studying the sleep habits of college males in the United States. Our population would consist of all males who attend college in the U.S.
Since there are over 9 million men in this population, it would be virtually impossible for us to collect data from every single member. But how then do we collect our research data? We collect data from a sample, which is a portion of the population that's used to represent the entire population. In order to study our population, we can take 560 U.S. college males and collect data from them.
A sample allows us to collect data from a few members that represent the whole population. When a sample is truly representative of a population, we can make inferences that apply to the entire population. The best way to obtain a representative sample is through the use of a random sample.
In order for our sample to be random, two things must occur:
- Every member of the population must have an equal chance of being selected to be a part of the sample.
- The selection of one member of the population is not dependent upon the selection of another member.
Suppose you wanted to research the effects of receiving a bad grade on the morale of 100 high school freshmen. You might assign each student a number from 1-100, place the numbers in a hat, then choose 30 numbers from the hat to be in your sample. This is known as the lottery method. Because every person has the same chance of being picked, it helps make sure that all possible biases and viewpoints are represented. Since each number was only placed on one piece of paper and all the papers were the same size, each person had the same chance of being picked. Also, since the numbers were written on separate sheets of paper, the selection of one person was not dependent on the selection of another.
Advantages and Disadvantages
There are several advantages to using a random sample, including:
- It's highly representative of the population
- It decreases bias since the process of selecting the sample is done randomly
- Data analysis and interpretation is simpler in random samples
There are also several disadvantages to using a random sample, including:
- It's not possible if you do not have a complete list of the members in the population
- It can take a lot of time and money if you are sampling data from a large population
- The characteristics of the sample and/or the population can change over time
Let's review. In a random sample, each member of the population has the same probability of being selected for the sample. In addition, the selection of one member of the population is completely independent of the selection of another. There are several advantages to using a random sample including a decrease in bias and having data that is highly representative of the population. There are also disadvantages, including the potential costs, time that it may take to find a truly representative sample, and the difficulty of creating a complete list of all members in a population.