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Comment by datashow | Hacker News Reader

This is a crappy psychology study where none of the researchers is a psychologist. PNAS is a big name in medical science (?), but an unimportant journal in psychology.

There's no serious psychology journal going to publish this kind of stuff. Why? The first problem is measurement. What is the reliability of using positive/negative words to determine positive/negative emotional state? 70%? 80%?

The effect size of this study is 0.001, which would be way way way smaller than the measurement error. LOL. What a laughable "study".

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Completely agree with you that the study is trash, however ...

>but an unimportant journal in psychology.

This is untrue. It's comically untrue.

I was doing my PhD in cogsci when this came out and everybody was surprised that PNAS would publish such a bad study, given that we were more used to seeing things like this: https://www.pnas.org/content/pnas/106/5/1672.full.pdf and this https://www.pnas.org/content/pnas/112/2/619.full.pdf.

PNAS has an _excellent_ reputation in psychology, especially in the psychophysics and EEG/MEG crowd.

PNAS does this sort of thing often enough, it doesn't really deserve its positive reputation (if any): https://statmodeling.stat.columbia.edu/?s=pnas

Adam Kramer has a PhD in psychology, for what it's worth.

Thanks. Good to know.

cognitive science is a unique field, it is somewhat related to psychology, but definitely not a subfield of psychology.

In the psychological sciences, it seems like you're damned if you do and damned if you don't.

When a phenomenon with a large effect size is demonstrated with tens or hundreds of participants, everybody crows about how the sample size should have been larger.

On the other hand, when a small effect size requires millions of observations to detect, now the criticism is that the effect is too small to matter.

At any rate, this effect is small - but it is reliable. The only crappy part about this study is the ethical boundaries it crossed. In most other ways, this study was kindof amazing...

In statistics, there is an optimal sample size for avoiding type 1 and 2 errors.

https://www.stat.ubc.ca/~rollin/stats/ssize/n2.html

There was a tiny difference detected with millions of observations. But it has no scientific meaning.

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