Hack Your Way to Scientific Glory (2016)

An excellent demonstration of how easy it is to manipulate data to find significant results - it brings to light (again) the pervasive issue of data dredging in research, where researchers, intentionally or unintentionally, keep testing hypotheses until they find something publishable, thereby undermining the integrity of scientific findings and highlighting the importance of preregistration and replication studies.<p>How can we shift the academic incentives away from &quot;publish or perish&quot; toward promoting transparency and rigorous methodology in research? Are any of the current attempts moving the needle?

I know the exercise was to p-hack, but instead I decided to one-shot my attempt at the most reasonable model from first principals:<p>- given that we are looking at a national scale, use only national politicians<p>- use the components from Macroeconomics 101: exclude inflation as that’s on the Fed, exclude stocks as too conflated with FX and international investing alternatives<p>- don’t needlessly withhold data<p>Tried one hypothesis, so p-value of 0.04 is accurate. Still OK to explore if you Bonferroni correct the p-Val afterwards

For anyone wondering about the title, it&#x27;s about p-hacking - picking and choosing variables to suit your objectives. Highly recommend trying the mini-game, it&#x27;s fun!

They say a photo is worth a thousand words, and here, this demo is as convincing as a lot of the spilt ink regarding the replication crisis and social psychology. As someone who pursuing a PhD in the social sciences... This could be a useful tool for seniors majoring in the social sciences &#x2F; who are taking stats classes.

Sigh. P hacking has long been a meme. Nicholas Cage and shark attacks for example. P values are useful though if the results seem at least plausible or can be explained by some underlying process. Like a low p value between calorie restriction and weight loss.