The set of probabilities associated with each possible outcome in the sample space is referred to as what?

The set of probabilities associated with each possible outcome in the sample space is referred to as a probability distribution. This concept serves as the foundation for probability theory and is essential for statistics, particularly in modeling random variables. A probability distribution illustrates how probabilities are distributed among different possible outcomes, which can be discrete (e.g., rolling a die) or continuous (e.g., measuring the height of individuals). It provides a comprehensive overview of how likely each outcome is to occur, making it a fundamental tool for understanding random phenomena in healthcare statistics and beyond.

Other concepts, such as t distribution, conditional distribution, and marginal distribution, represent more specific statistical scenarios and are not general representations of probabilities for all outcomes in a sample space. The t distribution is used primarily in hypothesis testing and confidence intervals for normally distributed populations with small sample sizes. Conditional distribution pertains to the probability of an outcome given that another event has occurred, while marginal distribution focuses on the probabilities of a subset of variables in a larger multivariate context. Each of these is important in statistical analysis but does not encapsulate the idea of a complete set of probabilities for all possible outcomes as a probability distribution does.