Great article on why one shouldn't think p-values are the gold-standard when it comes to evaluating experiments and conduct hypothesis-testing: http://www.nature.com/news/scientific-method-statistical-errors-1.14700
It proposes to report effect-sizes and confidence-intervalls when reporting (publishing) results of experiments and tests. Additionally one should look to evaluate tests not only the "frequentist" way but also incoporate bayesian evaluations of tests. The following links are good tutorials on how to do this in R:
http://www.win-vector.com/blog/2013/05/bayesian-and-frequentist-approaches-ask-the-right-question/
http://www.sumsar.net/blog/2014/01/bayesian-first-aid/
The nature-article also talks about p-hacking, but doesn't go into detail about the specifics on how to hack your p-value. A common mistake is to look into the test-results on a daily basis and to stop the experiment whenever a p-value smaller 0.05 has been reached. Multiple comparisons is a huge issue when looking at p-values of a test.
The best summary on all the fallacies on can encounter while doing ab-tests is this: http://www.evanmiller.org/how-not-to-run-an-ab-test.html
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