This chapter emphasizes the importance of testing whether results hold under different assumptions, conditions, or analyses. Robustness checks prevent false confidence in findings and strengthen their credibility. The chapter discusses techniques such as sensitivity analysis, subgroup analysis, and replication in varied contexts. Examples show how robustness checks in architectural experiments—such as testing signage effectiveness across visitor types—reveal whether effects are generalizable or context-specific. By integrating these checks, researchers avoid overstating results and ensure their recommendations withstand scrutiny in both academic and practical settings.