Stratified Randomization
Stratified randomization is a sampling technique used in experimental design to ensure that each subgroup or stratum of the population is represented in the sample in proportion to its size in the population. In the context of data science and artificial intelligence, stratified randomization is commonly used in the design of experiments to ensure that the treatment and control groups are balanced with respect to important covariates or variables that may affect the outcome of the experiment. This technique helps to reduce bias and increase the precision of the estimates obtained from the experiment. Stratified randomization is particularly useful in situations where the population is heterogeneous and there is a risk of confounding variables affecting the results of the experiment.
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