Today I tried understanding the Monte Carlo method, since it was discussed in a previous class. What I understood is that the Monte Carlo method is a technique used to estimate uncertain outcomes through randomness and simulation. I further performed this test on the diagnosed diabetes dataset for urban and rural counties and the p-value came out to be 0.99999.
This finding confused me a bit because, in an earlier analysis using a t-test on the same datasets, I had obtained a drastically different p-value of 3.832914332163736e-20.
This contrast in p-values prompted me to delve deeper into the discrepancy between the two methods. It became apparent that the Monte Carlo method can yield variable results based on the assumptions made during the simulations. On the other hand, the t-test assumes a normal distribution of data, and if this assumption is not met, the reliability of the results may be compromised.
To address this disparity in p-values and gain a better understanding of the data, further exploration and analysis are necessary. This involves scrutinizing the dataset, investigating potential outliers, and considering alternative statistical tests to get clarity on the reasons behind the differences in results and draw more accurate conclusions from the data