Sadly, the concept of p-values and significance testing forms the very core of statistics. A number of us have been pointing out for decades that p-values are at best underinformative and often misleading. Almost all statisticians agree on this, yet they all continue to use it and, worse, teach it. I recall a few years ago, when Frank Harrell and I suggested that R place less emphasis on p-values in its output, there was solid pushback. One can’t blame the pusherbackers, though, as the use of p-values is so completely entrenched that R would not be serving its users well with such a radical move.
The American Statistical Association (ASA) has released a “Statement on Statistical Significance and P-Values” with six principles underlying the proper use and interpretation of the p-value [http://amstat.tandfonline.com/doi/abs/10.1080/00031305.2016.1154108#.Vt2XIOaE2MN]. The ASA releases this guidance on p-values to improve the conduct and interpretation of quantitative science and inform the growing emphasis on reproducibility of science research. The statement also notes that the increased quantification of scientific research and a proliferation of large, complex data sets has expanded the scope for statistics and the importance of appropriately chosen techniques, properly conducted analyses, and correct interpretation.
I personally think Big Data needs Bigger Thinking among statisticians about a new era of inference.
However as always these guys are the best