This paper studies the optimal adaptive sample allocation for finding the best among a finite set of options with the highest statistical guarantees and in the quickest time. With the growth in the number of experiments that companies run, the existence of an optimal allocation algorithm that reduces the required experimentation time (and hence the cost) while providing statistical confidence in the result is crucial. Old frequentist hypothesis testing methods are primary methods for addressing this problem and they provide statistical guarantees for the decision. These algorithms work poorly when the number of options are large, which unfortunately is the case in many practical applications. Multi-armed bandit methods are adaptive allocation rules designed for maximizing a predefined reward function that depends on the allocation during the experiment. Although bandit algorithms are shown to be optimal for the reward maximization, they cannot provide statistical confidence for the decision they make. Best-arm identification algorithms are adaptive exploratory allocation rules that achieve a compelling performance in finding the winner but they also fail to provide statistical guarantees. In this paper, we introduce OptoAllocate Confidence Interval (OACI), an adaptive allocation rule that assigns samples among options such that the confidence interval of the winning option has the largest separation from all other options’ confidence intervals. Simulations show that in many scenarios, OACI finds the winner of the test with a higher accuracy and with a better statistical guarantees than the variants of hypothesis testing, multi-armed bandit, or best-arm identification algorithms.