I am a recent graduate of a biostatistics program working as an analyst for health-related studies. We were taught to always "examine the data" before conducting an analysis by generating summary statistics to determine if the analysis you plan to run is appropriate for the data you have, has bias, could be done in a more valid way, etc. I still don't understand... What exactly am I looking for that can improve my analysis methods?

I understand that each method of analysis has a set of assumptions, and these assumptions must be met for the method to produce valid results. But it seems like there's SO much more to be learned from descriptive statistics tables than I can see. I can ensure that the data meets these assumptions, and still someone far more senior than me will say something like "Ah yes, but there isn't enough variability between these two categories of this variable and so the estimates for these categories won't be reliable."

Say, for example, I plan on running a Cox proportional hazards model. I have generated a summary table which shows the frequencies and means of my covariates for event=0 and event=1 of my outcome. This type of descriptive statistics table is a staple in pretty much every peer-reviewed observational study ever. What is the point of this table? What information can I glean from this table that will affect how I will run my Cox model?

I have tried searching online and in books for what feels like days for even one resource that can help me understand what to look for in the data that can help me improve my analysis methodology, but the only resources I could find were not for people actually doing the analysis (not technical enough).

I feel as if I'm going insane. What have I missed?!