The article explores how much data is needed to accurately estimate a Gaussian model. It focuses on the impact of sample size and the dimension of space on the accuracy of likelihood estimates. The researchers highlight that while maximum likelihood estimates are reliable with infinite data, their behavior with small sample sizes is uncertain. The study emphasizes the importance of having enough data to ensure accurate estimation of the Gaussian model, especially when dealing with ill-conditioned data.