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Randomization is a statistical method that randomly assigns participants to experimental or control groups, ensuring each participant has a fair chance of selection. It eliminates selection bias and balances known and unknown confounding factors, making the control group similar to the treatment group. This prevents bias from subject selection and accidental errors during and after the experiment.

Simple randomization uses a single random sequence, such as flipping a coin or rolling a die, to assign participants to groups. Each selection remains completely random with no predetermined structure. Unlike block or stratified methods, simple randomization does not account for sample characteristics or ensure balanced group sizes across time.

Block randomization randomly assigns participants to groups while ensuring equal sample sizes across groups over time. It is used when sample characteristics, such as gender, may affect outcomes and act as blocking variables. Each block is small and balanced with predetermined group assignments, so participant numbers remain similar in each group.

Stratified randomization groups and balances prognostic variables, such as gender and body mass index, to control for covariates. Researchers identify specific covariates and create separate blocks for each combination, called strata. Participants are assigned to appropriate covariate blocks, then randomly assigned to treatment or control groups within each stratum.

Random number generators are computer programs used to assign participants to groups in a way that minimizes bias. They provide an objective, automated method for randomization that removes human judgment from the assignment process. This ensures truly random allocation and maintains the integrity of the randomization method across all participant assignments.

Balancing sample characteristics ensures that known and unknown confounding factors are distributed equally between treatment and control groups. When characteristics like gender or body mass index differ between groups, they can act as blocking variables that affect outcomes independently of the treatment. Balanced samples strengthen causal inference by isolating the treatment effect from other influences.

Randomization prevents selection bias by removing human judgment from participant assignment to groups. When participants are randomly assigned rather than selected based on characteristics or convenience, researchers cannot unconsciously favor certain individuals for specific groups. This objective process ensures that controls in experiments remain unbiased and that observed differences result from treatment, not pre-existing differences.