Randomization in Controlled Experiments
Summary
ACM Queue's Randomization in Controlled Experiments discusses how randomization minimizes bias and enables causal interpretation in experiments. It covers methods such as simple and stratified randomization, blocking, factorial designs, and adaptive designs, along with practical considerations for sample size and power. The piece offers guidance relevant to AI model evaluation and software experimentation.