randomizations

Randomizations are procedures that assign experimental units to treatment conditions by chance. They are used in experimental research to reduce selection bias and to support causal inference about treatment effects. Randomization is distinct from random sampling of participants; its primary goal is to create comparable groups with respect to both observed and unobserved factors.

Common methods include simple randomization (equal probability for each unit), block randomization (randomization performed within blocks to maintain balance), stratified randomization (randomization within strata defined by prognostic factors), and cluster randomization (randomization of groups or clusters rather than individuals).

In the design phase, allocation concealment and blinding often accompany randomization to prevent selection bias and measurement bias. Analyses may use the randomization distribution, or perform randomization or permutation tests, which rely on the known randomization procedure to compute p-values.

Randomizations are also used in simulations and computational methods, notably in Monte Carlo studies, bootstrapping, and permutation tests. Random seeds ensure reproducibility of results.

Limitations include the possibility of imbalance by chance, especially with small samples; cluster randomization requires accounting for intra-cluster correlation; noncompliance and missing data can complicate analysis. Proper planning, sample size considerations, and reporting of the randomization method are essential for credible results.