This chapter discusses the validity and reliability of discrete choice experiments (DCEs) in environmental valuation. It outlines three key aspects of validity: content validity, construct validity, and criterion validity. Content validity ensures that the DCE survey accurately reflects the true values of the target population. Construct validity involves testing whether the model's assumptions align with economic theory, such as the negative effect of cost on choice probability and decreasing marginal utility of income. Criterion validity compares DCE results with other established methods to assess their accuracy. Reliability refers to the consistency of results across repeated surveys, and methods like test-retest studies are used to evaluate this. The chapter also covers model comparison and selection, emphasizing the importance of statistical criteria like AIC and BIC, as well as cross-validation for assessing predictive performance. Prediction in DCEs is discussed, highlighting that models can only estimate the probability of choices, not the actual choices. The chapter concludes by emphasizing the need for thorough validation and reliability checks to ensure that DCE results reflect true preferences.This chapter discusses the validity and reliability of discrete choice experiments (DCEs) in environmental valuation. It outlines three key aspects of validity: content validity, construct validity, and criterion validity. Content validity ensures that the DCE survey accurately reflects the true values of the target population. Construct validity involves testing whether the model's assumptions align with economic theory, such as the negative effect of cost on choice probability and decreasing marginal utility of income. Criterion validity compares DCE results with other established methods to assess their accuracy. Reliability refers to the consistency of results across repeated surveys, and methods like test-retest studies are used to evaluate this. The chapter also covers model comparison and selection, emphasizing the importance of statistical criteria like AIC and BIC, as well as cross-validation for assessing predictive performance. Prediction in DCEs is discussed, highlighting that models can only estimate the probability of choices, not the actual choices. The chapter concludes by emphasizing the need for thorough validation and reliability checks to ensure that DCE results reflect true preferences.