The Monte Carlo approach is a method used to solve complex problems through random sampling. In simple words, it’s like making guesses repeatedly to find answers. We can use this approach with our dataset to simulate and analyze different scenarios. For example, if we’re studying the impact of a policy change on our dataset, we can create multiple random variations of our data, apply the policy change to each variation, and see how it affects the outcomes. By running many simulations, we can get a sense of the range of possible results and their probabilities, helping us to make informed decisions or predictions based on our dataset. It’s like rolling a dice many times to understand the likelihood of different outcomes without having to physically roll the dice every time.