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Testing whether a variable of interest affects the outcome is one of the most fundamental problem in statistics and is often the main scientific question of interest. To tackle this problem, the conditional randomization test (CRT) is widely used to test the independence of variable(s) of interest (X) with an outcome (Y) holding other variable(s) (Z) fixed. The CRT uses randomization or design-based inference that relies solely on the iid sampling of (X,Z) to produce exact finite-sample p-values that are constructed using any test statistic. We propose a new method, the adaptive randomization test (ART), that tackles the independence problem while allowing the data to be adaptively sampled. We first showcase the ART in a particular multi-arm bandit problem known as the normal-mean model. Under this setting, we theoretically characterize the powers of both the iid sampling procedure and the adaptive sampling procedure and empirically find that the ART can uniformly outperform the CRT that pulls all arms independently with equal probability. We also surprisingly find that the ART can be more powerful than even the CRT that uses an oracle iid sampling procedure when the signal is relatively strong. We believe that the proposed adaptive procedure is successful because it takes arms that may initially look like "fake" signals due to random chance and stabilizes them closer to "null" signals. We additionally showcase the ART to a popular factorial survey design setting known as conjoint analysis. We find similar results through simulations and a recent application concerning the role of gender discrimination in political candidate evaluation.