Quasi-Experimental Study
A quasi-experimental study is a research design used in social sciences, education, and other fields to estimate causal relationships when random assignment of participants to treatment and control groups is not feasible or ethical. It mimics experimental designs by comparing groups that receive different treatments or conditions, but without the strict randomization of true experiments, often relying on natural or pre-existing group differences. This approach helps researchers draw inferences about cause-and-effect in real-world settings where controlled experiments are impractical.
Developers should learn about quasi-experimental studies when working in data science, machine learning, or product analytics to evaluate the impact of features, interventions, or policies in non-laboratory environments, such as A/B testing with non-random user segments or assessing software changes in production systems. It is crucial for making evidence-based decisions in tech companies, especially when ethical or logistical constraints prevent randomized controlled trials, allowing for robust analysis of observational data to infer causality.