I was lucky and figured it out before getting a degree. Its got to be hard for people in this position to look back on their previous work where the most fundamental aspect of interpreting the results was incorrect.
He gets it right that statistics are good for estimation, but there is a part two. You need to come up with a theory that makes a prediction to compare to these estimates, and then test that. Ie, your prediction about the distribution of the results is the "null hypothesis". I think p-values are probably ok for that.
I've met Steve Luck multiple times over the last decade. He is a very rigorous and insightful scientist, with expertise in a wide range of methods (especially eeg) and psychological phenomena. His wife Prof. Lisa Oaks may be even more impressive in these regards, but that is an aside. The point I would like to make though is that there are lots of aspects to research and stats is just one part to master of many. It should not be up to a neuroscientist to make posts like this. The stats community should be actively pushing the scientific community to alternatives that match interpretative intuition with reality of the statistical metric.
I went through the same thing. I was taught the same half-assed BS statistics with all the wrong interpretations and was surrounded by people just following along with that.
Still, it was a point of duty/honor for me to figure out what these statistical results meant. How is interpreting results not a key part of a scientists job? I guess if you don't want to bother learning to interpret results then you can do science by either being a lab tech or theorist.
[1]http://mindbrain.ucdavis.edu/people/sjluck
I was lucky and figured it out before getting a degree. Its got to be hard for people in this position to look back on their previous work where the most fundamental aspect of interpreting the results was incorrect.
He gets it right that statistics are good for estimation, but there is a part two. You need to come up with a theory that makes a prediction to compare to these estimates, and then test that. Ie, your prediction about the distribution of the results is the "null hypothesis". I think p-values are probably ok for that.