“Statistics cannot substitute for clear thinking. It can’t do the job of human inductive inference.” I’ll admit it up front, statistics has never been my forte. I belong to a generation that was poorly educated on the topic. The courses I took focused on frequentist probability. The lectures were aimed at giving us tools for generating publication worthy p-values rather than interpreting data to understand natural phenomena. This cookbook approach came across as unsatisfactory and counter-intuitive to the budding scientist I was, especially once I started generating my own experimental data and came to realize how messy biological experimentation and data can be. These days, I rely primarily on expert colleagues for their guidance. But I can deal with data much better than I used to. I also understand better what my job is about. My goal as a scientist is to produce knowledge that yields predictable outcomes, and my obsession isn’t with p-values but with reproducibility. Nothing beats controls and replication, especially when orthogonal replication with a different method independently validates a finding. I’m not going to build my reserach program based on a single experiment with borderline p-values. I keep steering my lab away from shaky findings, and over and over again, I have resisted the temptation of becoming enamoured with weak models no matter how exciting they were — or how significant the p-value is. I can now confidently report that this approach has served our research team quite well.