As the number of trials increases in a probability experiment, the experimental probability tends to approach the theoretical probability of the event. This phenomenon is explained by the Law of Large Numbers, which states that as the number of trials or repetitions of an experiment increases, the experimental results (or relative frequency of an event occurring) will get closer to the expected (theoretical) probability of that event.

In simple terms, with more trials, any anomalies or outliers in the results tend to be averaged out, and the outcomes become more predictable, aligning with the theoretical probability.

Would you like a more detailed explanation or have any other questions?

Here are 5 related questions:

1. What is the difference between theoretical and experimental probability?
2. How does the Law of Large Numbers affect probability in real-life scenarios?
3. Can experimental probability ever perfectly match theoretical probability?
4. How is experimental probability calculated in repeated trials?
5. What factors could cause experimental probability to deviate from theoretical probability?

Tip: Increasing the number of trials not only stabilizes the experimental probability but also helps reduce the effect of random fluctuations in small samples.