Comment by coppsilgold
12 hours ago
What should give people pause is how not complicated (I'd hesitate to say easy) it would be to create a python script that would generate fake data such that it would be all but impossible to determine whether it's real or not. You just need to model the measuring device and hypothesis you want to support, then sample away.
The people who get caught red handed like this are lazy, incompetent and stupid. Makes you wonder what about the ones not getting caught.
The other explanation is that often these are just mistakes that occur with a team of experts in their field but not data management, without a budget for building a more robust system, manually doing a lot of things with data. It's so easy to copy and paste something into the wrong place, to sort by a field and get things out of order, all kinds of issues like that.
On the other hand, any time a hypothesis appears significant, the first reaction should be to verify that all the data going into the calculation is correct, rather than just assume it is. In my day-to-day industry experience, significant results come far more often from incorrect data than an actual discovery.
Reminds me of a job I had doing QA at a pill factory in high school in the 90s. Weight, size, crush tests. After 3 months seeing 99.99% pass rates, I pulled out my statistics book and started generating fake data and just read books. If I could do it at 16...
It's likely easier to publish genuine research if you have knowledge and rigor to properly synthesize the data. It's not as common as you'd think, and a good simulator is easily publishable, at least was some years ago in domains I'm familiar with.
The conspiratory reason would be that copy-paste errors give plausible deniability of ill intent.
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> The people who get caught red handed like this are lazy, incompetent and stupid.
Being a cheat significantly correlates with laziness, incompetence and stupidity so there are probably very few cheats smart and diligent enough to not get caught.
The sample of cheaters that we know about is biased towards cheaters who get caught.
Based on my experience of the field, I very much challenge that assumption.
Indeed, the niches of smart cheats or smart criminals have a lot of room. Because the trajectories to reach that stage without being caught by a legal (good legal work) or moral (good person) attractor are sparse and that makes them somewhat rare.
Handwaving correlations between cheating/criminality and most personality/intelligence aspects is an error, not least because there is a selection bias problem (eg. who gets caught).
> Being a cheat significantly correlates with laziness, incompetence and stupidity
There is no evidence for this.