Statistical Decision Theory is a framework used to make decisions under uncertainty by applying statistical methods. It combines probability theory and statistics to evaluate different choices based on their potential outcomes and associated risks. This approach helps in identifying the best decision by considering both the likelihood of various scenarios and their consequences. In Statistical Decision Theory, decisions are often represented using a decision tree or payoff matrix, which outlines possible actions, states of nature, and their respective payoffs. By analyzing these elements, decision-makers can choose the option that maximizes expected utility or minimizes potential losses, leading to more informed and rational choices.