A big part of what I do involves writing data-driven blog posts about trends in technical recruiting. I was really struggling to do the data analysis portion of a blog post, doing R tutorials and so forth, when I came across Statwing. Statwing ended up being game-changing and made it possible for me to churn out content I was really proud of a lot faster -- it's probably not an exaggeration to say that Statwing saved me a few weeks' worth of work for each post. I now find myself relying on Statwing as a sort of exobrain wired for statistics -- instead of having to agonize over which parts of the data I should analyze, I can do ALL THE ANALYSIS without tradeoffs, lowering the barrier to finding delightful and unexpected outcomes. In some sense, Statwing almost makes things too easy, and it's important to remind yourself that statistical significance or a strong correlation do not necessarily make for insights and that there could always be selection biases, a third variable controlling everything behind the scenes, Simpson's paradox, or some other beastie.

I used to use SPSS to do this kind of analysis. I didn't think it was that bad at the time, but after using Statwing I'm blown away by the difference. It's just a lot easier to use, much more intuitive, and I get things done much more quickly. The only knock is that it doesn't have more sophisticated analyses like PCA or survival analysis. But I personally only needed the basic + regression analysis, so it worked well for me.