12-24-2025, 09:33 AM
I'm a biology PhD student analyzing a complex dataset from a longitudinal ecological study, and while I'm comfortable with basic statistical tests, I'm struggling to apply the correct principles of statistical inference to draw valid conclusions about causal relationships from my observational data. I understand the difference between frequentist and Bayesian frameworks conceptually, but I'm unsure how to choose between them for my specific hypothesis about species interaction, or how to properly account for spatial autocorrelation and multiple comparisons in my models. For researchers who regularly work with messy real-world data, what is your practical workflow for moving from raw data to robust inferential statements? How do you decide which assumptions are critical to test and which modeling trade-offs are acceptable, and what resources would you recommend for building a stronger intuitive grasp of inference beyond textbook examples?