On Marc Hauser and Science

Things are looking very bad for Marc Hauser(via Language Log), and it's been sending shivers up and down the spines of a lot of people I know. What he's been accused of seems like the worst thing that could happen. It's like learning that someone from your neighborhood is a serial killer; the stuff of hushed conversations in the hallway. "Oh my god! I was just about to cite that paper too. And I know a guy whose brother was an RA in his lab. I mean, everyone knew some of this stuff was a stretch, but this?"

I'd like to reflect on what this news means for science. Hauser's behavior was, if the final verdict ends up being what it looks like it's going to be, completely reprehensible. Fudging data is antithetical to the progress of science, and extremely damaging to public understanding of science. To quote Shaggy 2 Dope from the Insane Clown Posse:

Fucking magnets. How do they work? And I don't want to talk to a scientist. Y'all motherfuckers lying, and getting me pissed.
Insane Clown Posse, Miracles

The proliferation of attitudes like Mr. Dope's makes Hauser's (alleged) actions even more shameful, especially as it seemed like he tried to craft a lot of his work for broader public consumption.

But at the end of a day, even though a scientist failed, science was successful. According to the Chronicle of Higher Ed's story, research assistants in Hauser's lab successfully utilized the scientific method to discover inconsistencies in the data. Hauser's coding was not reproducible from the raw data, eventually leading one of his research assistants to report him to higher ups at Harvard. Here is a clear case of scientific progress at work, with no regard for the status or authority of the researchers involved.

And, I think the consequences will be positive, over all. This should serve as a reminder to us all of the importance of reproducibility or our research. There will be broader demand to demonstrate that we are not Hausering the data (you heard it here first!), which means publishing more raw data, which means cleaner, more careful research.