Randomly assigning participants to groups means using a chance-based process, like a coin flip or a random number generator, to decide which group each person joins. This ensures the groups start out comparable, so any differences you observe at the end can be attributed to the treatment rather than preexisting differences between people. The specific method you choose depends on your sample size, how many variables you need to balance, and how complex your study design is.
Why Random Assignment Matters
Random assignment is what separates a true experiment from an observational study. When you let chance decide who goes where, you distribute both known and unknown characteristics evenly across groups. Age, health status, motivation, genetics: all of these get spread out roughly equally without you having to account for each one individually. That equal distribution is what allows you to say your treatment caused the outcome, not some other factor lurking in the background.
Without random assignment, selection bias creeps in. A researcher might unconsciously place healthier-looking participants into the treatment group, or participants might self-select into conditions that suit them. Randomization is the only reliable mechanism to eliminate that kind of systematic error for both measured and unmeasured confounders.
One common point of confusion: random assignment is not the same as random selection. Random selection is about who you recruit into your study from a larger population, which determines whether your results generalize broadly. Random assignment is about what happens after recruitment, when you split your participants into groups. Ideally you do both, but they serve different purposes. Random selection lets you generalize. Random assignment lets you infer causation.
Simple Randomization
The most straightforward method is simple randomization, where each participant has an equal chance of landing in any group, with no restrictions or conditions. The classic version is literally flipping a coin: heads goes to the treatment group, tails goes to control. You can also use a shuffled deck of cards (even cards for one group, odd for the other) or roll a die (1 through 3 for control, 4 through 6 for treatment).
For anything beyond a classroom exercise, most researchers use a random number generator. You can generate a sequence of random numbers using software like Excel, R, or Python, or use a free online tool. Assign each participant a random number, then sort by that number and split the sorted list into groups. The National Cancer Institute offers a dedicated clinical trial randomization tool that generates recruitment lists with built-in randomization procedures.
Simple randomization works well when your sample is large, because the law of large numbers ensures the groups will end up roughly equal in size and composition. The problem shows up with smaller samples. With 20 participants and a coin flip, you could easily end up with 13 in one group and 7 in the other. That imbalance reduces your study’s statistical power and can undermine your results.
Block Randomization
Block randomization solves the unequal group size problem. Instead of randomizing all participants in one long sequence, you divide the process into smaller blocks. Within each block, an equal number of participants are assigned to each group.
Here’s how it works in practice. Say you have two groups and you choose a block size of 4. Within each block of 4 participants, exactly 2 will go to the treatment group and 2 to control. The order within the block is randomized, so you might get sequences like AABB, ABAB, ABBA, and so on. Every time a block is completed, the groups are perfectly balanced. When the overall sample size is a multiple of the block size, you’re guaranteed equal group sizes at the end.
A refinement is to vary the block sizes randomly (for example, alternating between blocks of 4 and 6). This makes it harder for researchers to predict upcoming assignments, which matters when you’re trying to keep the process concealed from the people enrolling participants.
Stratified Randomization
Sometimes you know in advance that certain characteristics could strongly influence your outcome. If you’re studying a rehabilitation technique after surgery, for instance, patient age affects healing speed. If one group ends up significantly older than the other by chance, age becomes a confounding variable that muddies your results.
Stratified randomization addresses this by sorting participants into subgroups (strata) based on key characteristics before randomizing. You identify the variables that matter most, create categories for each, and then randomize separately within each category. For a study with 40 participants where sex (male or female) and body mass index (underweight, normal, or overweight) are important covariates, you’d create six possible combinations: male/underweight, male/normal, male/overweight, female/underweight, female/normal, and female/overweight. Participants are placed into their matching stratum, then randomly assigned to treatment or control within that stratum.
The result is that both your treatment and control groups end up with similar proportions of men and women, and similar distributions of body mass. Block randomization is often used within each stratum to keep group sizes equal. The researcher needs to choose stratification variables carefully, though. The method works best with a small number of important covariates. Too many strata and you end up with very few participants per block, which defeats the purpose.
Covariate-Adaptive Randomization
When you have multiple influential covariates and a limited sample size, stratified randomization can break down because the number of possible combinations becomes unmanageable. Covariate-adaptive randomization, often called minimization, takes a different approach. Instead of creating strata in advance, it assigns each new participant dynamically by looking at who has already been assigned and choosing the group that would minimize the overall imbalance across all covariates simultaneously.
For example, if your treatment group is currently short on younger females and a younger female enrolls, the algorithm would weight the assignment toward the treatment group. This happens in real time as each participant enters the study. The method is increasingly used in trials where multiple covariates need balancing but the sample isn’t large enough for stratification to work reliably.
Keeping the Process Concealed
Generating a random sequence is only half the battle. If the person enrolling participants can see or predict the upcoming assignments, they might consciously or unconsciously influence who gets enrolled next. This is called allocation concealment, and it’s a separate concern from randomization itself.
The most common low-tech method is sealed opaque envelopes. Each envelope contains a pre-generated treatment assignment. When a participant consents to join the study, the next envelope is opened and the assignment is revealed. This works but has known weaknesses: envelopes can be held up to light, opened and resealed, or opened out of order.
A more rigorous approach is distance randomization. The researcher enrolling participants calls a central randomization service, provides the participant’s basic details, and receives a treatment assignment over the phone. This creates a layer of separation between the person recruiting and the allocation process. The same principle now works over the internet, with web-based systems generating assignments in real time that the enrolling researcher cannot predict or manipulate in advance.
Step-by-Step: A Practical Walkthrough
If you’re setting up a study with two groups and 30 participants, here’s one reliable approach using block randomization:
- Choose your block size. For two groups, a block size of 4 or 6 works well. Using a block size of 6 means every 6 participants will split 3 and 3.
- Generate the sequence. List all possible orderings of 3 A’s and 3 B’s within a block. Use a random number generator to select one ordering per block. Repeat for 5 blocks (5 blocks of 6 = 30 participants).
- Assign participants in order. As each participant enrolls, assign them the next letter in the sequence. A = treatment, B = control.
- Conceal the sequence. Place each assignment in a sealed opaque envelope numbered 1 through 30, or use an online randomization tool that reveals assignments one at a time.
If you also need to balance for a key variable like sex, add a stratification layer. Generate one block sequence for male participants and a separate one for female participants. When a male enrolls, use the next assignment from the male sequence. When a female enrolls, use the next from the female sequence.
For studies with very small samples (under 20) or several covariates to balance, consider a covariate-adaptive approach using dedicated software rather than attempting manual randomization, which becomes error-prone as complexity increases.