Why Randomize Study Groups?
Randomization is the gold standard for eliminating selection bias in experimental research. Without it, confounding variables — known and unknown — can systematically differ between groups, making it impossible to attribute outcomes to the treatment.
In animal studies, randomization is particularly important because researchers often select animals from the same breeding colony. Subtle differences in cage position, handling order, or weight can bias results if not properly controlled.
Simple randomization assigns each subject to a group with equal probability. Covariate-balanced randomization goes further — it ensures that measurable confounders (like body weight) are evenly distributed across groups.
How the Algorithms Work
The Group Randomizer uses a Fisher-Yates shuffle powered by a seedable PRNG (mulberry32). For simple randomization, subjects are shuffled once and dealt round-robin into groups.
For covariate-balanced randomization, the tool performs 1,000 Monte Carlo iterations — each time shuffling the subjects, dealing them into groups, and computing the maximum pairwise mean difference in body weight. The allocation with the smallest difference wins.
The Latin Square Generator creates either standard or balanced (Bradley, 1958) Latin squares. Standard squares ensure each condition appears once per row and column. Balanced squares additionally control for first-order carry-over effects — critical for crossover and within-subjects designs.
All algorithms are deterministic. The same seed + inputs always produce the same output. This is the foundation of the audit trail — any reviewer can independently verify your randomization.
