Quick Answer: Random assignment ensures that experimental groups are equivalent at baseline — distributing all known and unknown confounders equally across conditions. This allows researchers to attribute outcome differences to the treatment being tested, not to pre-existing group differences. It is the defining feature of a true experiment.
Random Assignment vs Random Sampling
These are different concepts often confused: Random sampling selects who participates in a study (from a population). Random assignment determines which condition each participant receives (within the study). Sampling affects external validity (can results generalize?). Assignment affects internal validity (do results reflect the treatment, not confounders?).
What Random Assignment Controls For
By randomly assigning participants to conditions, researchers ensure that any pre-existing differences between participants (age, health, attitudes, socioeconomic status) are distributed randomly across groups. On average, these characteristics balance across groups — so outcome differences can be attributed to the treatment, not to pre-existing group differences.
Types of Randomization in Research
- Simple randomization: Each participant assigned by coin flip equivalent
- Block randomization: Balanced in blocks (first 10 participants: 5 treatment, 5 control)
- Stratified: Randomize within subgroups (randomize men and women separately)
- Cluster: Assign whole groups (schools, clinics) rather than individuals
Consequences of Not Randomizing
Without random assignment, researchers cannot rule out confounders — pre-existing differences that explain the outcome. History is full of treatments that appeared effective in non-randomized studies but failed in randomized trials. The dramatic difference in conclusions between observational and randomized evidence led to the widespread adoption of RCT as the gold standard.