Quasi-Experimental Designs
Quasi-experimental designs are research methodologies used to estimate causal relationships when random assignment of participants to treatment and control groups is not feasible or ethical. They involve comparing outcomes between groups that receive different treatments or conditions, but without the strict randomization of true experiments, often relying on natural or pre-existing group differences. These designs are common in fields like education, public health, and social sciences where controlled experiments are impractical.
Developers should learn quasi-experimental designs when working on data science, analytics, or research projects that require evaluating the impact of interventions, policies, or features without the ability to conduct randomized controlled trials. For example, in A/B testing where random assignment is limited, or in observational studies analyzing user behavior changes after a software update. They provide a structured approach to infer causality from non-experimental data, helping make evidence-based decisions in product development or policy analysis.