Randomized experiment

Randomization reduces bias by equalising other factors that have not been explicitly accounted for in the experimental design (according to the law of large numbers).

Randomization also produces ignorable designs, which are valuable in model-based statistical inference, especially Bayesian or likelihood-based.

Web sites can run randomized controlled experiments [2] to create a feedback loop.

Daniel preferred a vegetarian diet, but the official was concerned that the king would "see you looking worse than the other young men your age?

Then compare our appearance with that of the young men who eat the royal food, and treat your servants in accordance with what you see".

[10][11][12][13] Outside of psychology and education, randomized experiments were popularized by R.A. Fisher in his book Statistical Methods for Research Workers, which also introduced additional principles of experimental design.

Most commonly, randomized experiments are analyzed using ANOVA, student's t-test, regression analysis, or a similar statistical test.

In practice, it is not possible to observe both potential outcomes for the same individual, so statistical methods are used to estimate the causal effect using data from the experiment.