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Political scientists increasingly rely on conjoint experiments to simulate and comprehend the intricate decision-making process of voters when confronted with candidates differing on multiple dimensions. However, in implementing these experiments, researchers often use forced choices between candidates to elicit responses from participants, neglecting the real-world option for voters to abstain or cast a blank/none vote. This mismatch can lead to significant measurement errors and bias. We empirically demonstrate and evaluate the bias induced by forced-choice design in typical conjoint experiments, using existing conjoint data from published articles, a series of simulations, and an original conjoint experiment that randomizes different choice scenarios. We then propose a practical guide that incorporates substantive knowledge about specific contexts to help researchers avoid possible pitfalls and enhance the precision of their estimations.