Design-based methods have recently been developed as a way to analyze data from impact evaluations of interventions, programs, and policies. The approach uses the building blocks of experimental designs to develop impact estimators with minimal assumptions. The methods apply to randomized controlled trials and quasi-experimental designs with treatment and comparison groups. Although the fundamental concepts that underlie design-based methods are straightforward, the literature on these methods is technical, with detailed mathematical proofs required to formalize the theory. This brief aims to broaden knowledge of design-based methods by describing their key concepts and how they compare to traditional model-based methods, such as such as hierarchical linear modeling (HLM). Using simple mathematical notation, the brief is geared toward researchers with a good knowledge of evaluation designs and HLM.