In my experience around a lot of professional data analysts, shoddy scientific design is ubiquitous; I was definitely guilty of it all the time. A mentor gave m

In my experience around a lot of professional data analysts, shoddy scientific design is ubiquitous; I was definitely guilty of it all the time. A mentor gave me some advice that helped tremendously: formulate what you're trying to answer very precisely (i.e. the hypothesis), and remember you are only trying to answer that question during every step of your experiment. It's very easy to lose focus or rush through important steps that seem insignificant. This is dangerous because bad methodology often looks a lot like good methodology but leads to very different results. When you have a precise hypothesis, it's much simpler to look at a step and say "no, that's not answering the question correctly".