When analyzing simulation datasets, consider what you want to extract: equilibrium information and/or kinetic components. Equilibrium properties, such as free energy differences or populations of states, provide of the system's thermodynamic stability, while kinetic components, such as transition rates, help understand the dynamical behavior between states. Given that the probability of a protein adopting any one structure is proportional to the exponential of that structures energy, we are able to characterize how likely a protein is to adopt some states over others. The phase space of a protein's energies (or probabilities) and their corresponding structures are often referred to as an "energy landscape", with most likely states (such as those observed by crystallography) are named "ground" states due to being energy minima. A common pitfall is plotting raw histograms of structural features or conformational populations without any reweighting. This practice is misleading because such plots do not account for the initial structural relaxation, or that these replicas may not be following a Boltzmann distribution. If multiple starting structures were used, then these replicates may not appropriately mix to generate physically reasonable distributions. It is important to ensure that each structural starting point is simulated uniformly across the dataset, and state mixing and transitions are observed. In particular, these pitfalls may exist when estimating kinetic processes. Instead, transition counting between states and constructing transition matrices offer a more accurate picture of the system's kinetics, allowing you to extract meaningful information on state-to-state dynamics.