Answer:
The sample size of an experiment can have a significant impact on the probabilities in a probability model. A larger sample size generally produces more accurate estimates of the probabilities of an event. This is because a larger sample size provides more data points that can be used to calculate the probabilities, which reduces the likelihood of random variation skewing the results.
For example, if you are conducting a coin toss experiment and flip a coin 10 times, the probability of getting heads may not be exactly 50%. However, if you flip the coin 1,000 times, the probability of getting heads will be much closer to 50% because the larger sample size reduces the impact of random variation.
Therefore, a larger sample size can lead to more realistic estimates of the probability of an event because it reduces the impact of random variation and provides more data points for analysis. However, it is also important to note that sample size alone does not guarantee accurate estimates of probability, and other factors such as selection bias and the quality of the experimental design can also affect the accuracy of the results.
Explanation: