n general, how does the number of trials seem to affect experimental probability?

The number of trials in an experiment can significantly affect the experimental probability. Here's how:

1. More Trials, More Accuracy: The more trials conducted in an experiment, the closer the experimental probability gets to the theoretical probability. This is due to the Law of Large Numbers, which states that as the number of trials increases, the results are more likely to approach the expected value.

2. Reducing Random Chance: With a small number of trials, results can be skewed by random chance. For example, if you flip a coin twice, you could easily get heads twice in a row, which would give an experimental probability of 100% for getting heads. However, if you flip the coin 1000 times, it's much less likely that you'll get heads every time. The experimental probability will be closer to the theoretical probability of 50%.

3. Smoothing Out Variations: More trials help to "smooth out" the variations and anomalies that can occur in any experiment. This means that the experimental probability becomes a more reliable and accurate reflection of the true probability.

In conclusion, increasing the number of trials in an experiment generally leads to a more accurate experimental probability.