From an interesting call for papers:
“Scientists spend a lot of time testing a hypothesis, and classifying experimental results as (in)significant evidence. But even after a century of hot debate, there is no consensus on what this concept of significance implies, how the results of hypothesis tests should be interpreted, and which practical pitfalls have to be avoided. Take the fierce criticisms of significance testing in economics, take the endless debate about statistical reform in psychology, take the foundational disagreement between frequentists and Bayesians about what constitutes statistical evidence.”
(Link to the conference here).