What does a tree diagram help to visualize in statistical experiments?

A tree diagram is particularly effective for illustrating the outcomes of a multiple-step experiment because it systematically lays out all possible scenarios and their associated probabilities. Each branch of the tree represents a possible outcome of a step in the experiment, and subsequent branches further delineate the outcomes that follow. This structure allows for a clear visualization of the paths through which different sequences of events can occur.

In the context of probability and statistics, using a tree diagram helps in analyzing complex experiments where results depend on several sequential actions. By illustrating all possible combinations of outcomes, it becomes easier to calculate overall probabilities for various scenarios, which could represent joint probabilities of events occurring together.

The other options do not accurately convey the primary purpose of a tree diagram. For example, while it can show trends if filled with data, it does not primarily function as a graphical representation of trends. Furthermore, it is not limited to single-step experiments since its primary strength lies in breaking down multi-step scenarios. Lastly, while it assists in probability calculations through its visualization of outcomes, its main role is to lay out the multiple steps themselves rather than solely serving as a probability calculation method.