It’s called simple random sample because it’s simple to implement.

You can also assign numbers to participants, or treatments, and use a random number table to choose participants and treatment groups. Image: Dan Kernler |Wikimedia CommonsSimple Random Sampling is basically where you draw numbers from a hat, choose a card from a deck or a ball from a bingo machine. Simple random sampling of a sample “n” of 3 from a population “N” of 12. Because these biases are often hidden, or overlooked, specific randomization techniques have been developed for researchers: Hidden biases (like a subconscious preference for English names, names that sound like friends, or names that roll off the tongue) means that what you think is a random selection probably isn’t.

Arbitrarily choosing names from a list might seem random, but it actually isn’t. The word “random” has a very specific meaning in statistics. You can also run a variety of statistical tests on your data (to test your hypotheses) if your sample is random. For example, selection bias (where some groups are underrepresented) is eliminated and accidental bias (where chance imbalances happen) is minimized. If you use randomization in your experiments, you guard against bias. You can also assign treatments randomly to participants, by assigning random numbers from a random number table. For example, you might use simple random sampling, where participants names are drawn randomly from a pool where everyone has an even probability of being chosen. Randomization in an experiment is where you choose your experimental participants randomly.

Types of randomization