Protecting against sketchy research practices: p-values, effect sizes, and power analyses

In my statistics class this Spring, we had a discussion about scientific reproducibility and practices that can lead to p-hacking. Two points came up in the class discussion that I have been interested to test on a real dataset. The first point is that p-values for a model can fluctuate quite dramatically with the addition of each new piece of data. Without an a priori defined sample size, this can lead researchers to collect a set of data, run an analysis, and then collect more data if the p-value is not significant.