P-hacking https://blogs.plos.org/scicomm/2015/05/19/p-hacking-megan-head-on-why-its-not-good-for-science/ is when researchers play with the data, often using complex statistical models, to arrive at results that look like they're not random. "There's nothing wrong with having a lot of data and looking at it carefully," Althouse says. "The problem is p-hacking." To understand p-hacking, you need to understand p-values. P-values https://galton.uchicago.edu/~thisted/Distribute/pvalue.pdf are how researchers measure the likelihood that a result in an experiment did not happen due to random chance. They're the odds, for example, that your new diet is what caused you to lose weight, as opposed to natural background fluctuations in myriad bodily functions. Nonetheless, Malina says, "We believe that the overwhelming majority of scientists are committed to rigorous and transparent work of the highest caliber."
https://www.npr.org/sections/thesalt/2018/09/26/651849441/cornell-food-researchers-downfall-raises-larger-questions-for-science https://www.npr.org/sections/thesalt/2018/09/26/651849441/cornell-food-researchers-downfall-raises-larger-questions-for-science from Megan Head: a) P-hacking is when researchers analyse their results in multiple ways or multiple times until they get their desired result b) this is an issue because it can make us think that an effect or relationship is more important than it really is c) these could lead to policies or recommendations based on false results. For instance if their have been a lot of studies showing that a particular drug has no effects on a particular disease if the data were p-hacked it might actually look like the drug helps to prevent the disease, when in fact it doesn’t, then doctors would be prescribing medicines that don’t actually work. d) trust research that has been replicated lots of times more than one off studies. https://blogs.plos.org/scicomm/2015/05/19/p-hacking-megan-head-on-why-its-not-good-for-science/ https://blogs.plos.org/scicomm/2015/05/19/p-hacking-megan-head-on-why-its-not-good-for-science/