MCMC methods are a great tool for fitting data because they explore the whole parameter space and, more importantly, they are able to deliver uncertainties. Uncertainties are crucial because they show how reliable the fit is. When the fit looks reasonable and the uncertainties are not very high, you can claim that you were able to describe your data successfully. However, what happens when your fits do not look so good; either because the values are unrealistic in the context of the system you are studying, or because the uncertainties are too high for the fits to be reliable? Is it the data or the approach to fitting the data the cause of failing?
In this talk, I will talk about my personal experience using MCMC methods during the course of my PhD. Firstly, I will show through different examples how the physical interpretation of the fitted values and their uncertainties was crucial in solving problems in my research. Sometimes, failing to fit the data –especially due to high uncertainties– led me to new insights about system I was struggling to understand, even taking that project into a whole new direction. Secondly, I will talk about situations where I still struggle fit the data, and I will share my insights as to why.