Without a clear hypothesis, simulations risk producing data that is either unfocused, irrelevant, or worse, incorrect. A well-defined research question should guide not only the simulation setup and experimental design but also the subsequent analysis. This approach shifts the focus from simply running standard protocols or procedures from a tutorial to actively probing biological mechanisms, answering questions that matter, and advancing the field of understanding at the atomistic level. For instance, instead of just assessing "protein stability" through RMSD, one could ask, "What conformational states are associated with ligand binding and are they different from those of the unbound protein structure?" Such targeted questions help focus the analysis on novel insights, preventing over-reliance on default metrics. Beyond RMSD and RMSF: Employing Advanced Analysis Techniques While analyses like RMSD and root-mean-squared fluctuation (RMSF) are commonly used to assess structural deviations in a structure over time, they are often insufficient for providing a comprehensive understanding of molecular behavior. These metrics, though helpful, are basic and can miss important structural transitions or dynamic events. More advanced techniques like clustering, principal component analysis (PCA), time-lagged independent component analysis (tICA), or Markov state models (MSMs) should be used to capture and characterize the full range of biomolecular conformational states. Machine learning-based clustering techniques, such as t-SNE, enable a more refined exploration of complex biomolecular conformation states, particularly in identifying rare or transient conformations. This deeper analysis moves beyond static snapshots, which are inadequate in conferring confidence of a conformational state sampled over time, to reveal dynamic trends that better reflect the true behavior of the biomolecular system. Other dimensionality techniques, like the energy landscape visualization method (ELViM), may also be useful in this regard. More robust assessment of individual states can help drive other analysis that is specifically related to the biomolecule of interest, such as hydrogen bonding, secondary structure evolution, and rearrangements in internal geometry that lead to conformational change.