Randomization methods are needed to yield causal effects, account for confounding, and reduce bias
Differences in baseline characteristics between treatment groups of an experimental research design can drastically bias the results (either positively or negatively). If groups are significantly different at baseline on an important variable or risk factor, multivariate statistics should be used to adjust for the differences.
There is a primary assumption associated with employing randomization in experiments. By use of randomization, the groups are thought to possess equipoise, or are assumed to be equal at baseline. Reverse causality (effect-cause) bias is also deterred when using randomized designs. Finally, randomization methods can account for both unmeasured and measured confounding variables.
There are several different methods for randomizing study participants to treatment groups in experimental research designs.
Randomization methods in experimental research designs
Website for conducting randomization - randomizer.org
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