Are you just demonstrating overfitting when estimating using too little data? Or is there something deeper going on in your example? What does the bank have to do with anything?
The bank is context that gives us a prior probability. However, MLE does not consider a prior. So MLE can give results that are not very helpful in the real world. All it does is answer: What parameter value (in case the probability) of a head, makes the observed outcome most likely? But it considers all parameter values equally likely. In reality, we know that it is highly likely that a random coin from a bank is a fair coin. Thus, if we flip two heads, we are almost certain that it's still a fair coin. If, on the other hand, we flipped 10 heads in a row, we might start to wonder if somehow the bank gave you a trick coin. MAP is an alternative to MLE, arguably better in many situations: [https://www.cs.cmu.edu/~aarti/Class/10701_Spring23/Lecs/Lect....
