Florent Buisson has an interesting post on avoiding p-value calculations:
And indeed, I worked with highly-skilled data scientists who had a very sharp understanding of statistics. But after years of designing and analyzing experiments, I grew dissatisfied with the way we communicated results to decision-makers. I felt that the over-reliance on p-values led to sub-optimal decisions. After talking to colleagues in other companies, I realized that this was a broader problem, and I set up to write a guide to better data analysis. In this article, I’ll present one of the biggest recommendations of the book, which is to ditch p-values and use Bootstrap confidence intervals instead.
I’m a committed Bayesian (or at least a Bayesian who should be committed—depends on who you ask), so I’d consider this a big step forward.