The confidence intervals in computer Go are used in MCTS algorithm to select the potentially most promising moves that should be evaluated with Monte-Carlo simulations. Smart selection of moves for evaluation has the crucial impact on program’s playing strength. This paper describes the application of confidence intervals for binomial distributed random variables in computer Go. In practice, the estimation of confidence intervals of binomial distribution is difficult and computationally exhausted. Now due to computer technology progress and functions offered by many libraries calculation of confidence intervals for discreet, binomial distribution become an easy task. This research shows that the move-selection strategy which implements calculation of the exact confidence intervals based on discreet, binomial distribution is much more effective than based on normal. The new approach shows its advantages particularly in games played on medium and large boards.