Randomizing

Randomizing refers to the process of assigning units—such as people, plots, or experimental runs—to different groups or conditions by chance, rather than by systematic choice. The goal is to reduce selection bias, balance known and unknown confounders, and enable valid statistical inference about causal effects. Randomization is central to experimental design, particularly in randomized controlled trials, psychology experiments, and A/B testing.

Common methods include simple randomization, where each unit has equal probability of assignment; block randomization, which uses fixed-size blocks to maintain balance across groups; and stratified randomization, which samples within strata defined by characteristics such as age or baseline severity to improve balance on those covariates. Adaptive or response-adaptive randomization uses accumulating data to adjust allocation probabilities. Allocation concealment and blinding are important to prevent prediction of assignments and maintain integrity.

In computing and simulations, randomization relies on random number generation. Pseudo-random number generators produce sequences that pass statistical tests but are deterministic given a seed; true random number generators derive randomness from physical processes. Reproducibility is achieved by recording the seed. Randomization underpins Monte Carlo methods, bootstrapping, and permutation tests, which rely on random or resampled data to estimate distributions or p-values.

Limitations and considerations include the possibility that randomization does not perfectly balance groups in small samples, the need for adequate sample size, and risks of bias if concealment or blinding fails. Transparent reporting of methods and preregistration help ensure credibility. Ethical considerations require appropriate use of randomization in clinical and other experiments.