Design of Experiments > Randomization

## What is Randomization?

Randomization in an experiment is where you **choose your experimental participants randomly**. 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. You can also assign *treatments *randomly to participants, by assigning random numbers from a random number table.

If you use randomization in your experiments, you guard against **bias**. For example, selection bias (where some groups are underrepresented) is eliminated and **accidental bias** (where chance imbalances happen) is minimized. You can also run a variety of statistical tests on your data (to test your hypotheses) if your sample is random.

## Randomization Techniques

The word “random” has a very specific meaning in statistics. Arbitrarily choosing names from a list might *seem* random, but it actually isn’t. 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. Because these biases are often hidden, or overlooked, specific randomization techniques have been developed for researchers:

*simple to implement.*However, in practice, it’s tough to use because adequate sampling frames (lists of all possible participants) are sometimes difficult or impossible to find.

2. Permuted block randomization.

Sometimes, just choosing participants randomly isn’t enough. You might want to balance your participants into groups, or

*blocks*. Permuted block randomization is a way to randomly allocate a participant to a treatment group, while keeping a balance across treatment groups. Each “block” has a specified number of randomly ordered treatment assignments.

3. Stratified Random Sampling.

*from*those groups.

There are less popular randomization methods. You can find a full list of these sampling methods here: Types of Sampling.

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