I just wasted 2 hours writing about this, and I can't release it. The text is just luminously, stupidly angry. Basically one long threat with numbers in it. It's the single worst article I've read in years. I will content myself with this: let's thug it.https://twitter.com/jamesheathers/status/1142863287526277120 …
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We never got to this article. The herculanean efforts of
@sTeamTraen@OmnesResNetwork and@Research_Tim aside, there's only so much you can do. We stopped at FIFTY. ARTICLES. with errors and we were losing the will to live.pic.twitter.com/MRuFpANqe5
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So let's get to it now. We don't even need error detection tools. It's a study on cracker consumption. Everyone gets small packets or large packets. Same total.pic.twitter.com/jpnoK80oSU
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So, people ate from 0 (no crackers) to 400 calories, and this sub-group of overweight people (n=15) ate Mean=383 cals, SD=159? THIS IS IMPOSSIBLE, unless they were eating OTHER PEOPLE'S CRACKERS.pic.twitter.com/lixBfGL9wf
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Which isn't mentioned. It's implied that the calorie intake was calculated just by # of crackers per person after the experiment ("After watching the 22‐min show, participants completed questionnaires, and the remaining crackers were counted to calculate their caloric intake.")
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You don't even need basic error detection tools to determine this, just look at the graph provided. This is mean (**SEM**) for the package vs. BMI group for all four groups. As there is a hard limit at 400kcals, is there nothing suspicious about the large packet BMI>25 group??pic.twitter.com/yGELNfe5qp
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I will bet anyone a large sum of money that other errors are present, because the above took about NINETY SECONDS.
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I will leave you with this truly absurd quote (from the http://Inverse.com article again). "One of the 200 that did not get retracted." Words fail me.pic.twitter.com/8VwTEA3WIX
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I cannot fully describe just how contemptible and inaccurate I find this article, which reads as a partial rehabilitation of terrible science because it sounds fun and biscuit companies like it.
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Replying to @jamesheathers
James, I'm looking for ~recent examples of research that affect (or have affected) the day-to-day lives of people, in spite of being unforgivably bad. It's for a social sciences course on getting fooled by data. Any favourites/suggestions?
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The young people and mobile phone horns is a good one. Graph directly contradicts the supposed findings
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Replying to @GidMK @jamesheathers
Intrigued, not sure I've seen this. Got a link?
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Replying to @Heinonmatti @GidMK
That's everywhere right now. https://www.smithsonianmag.com/smart-news/cell-phones-are-probably-not-making-us-grow-horns-180972474/ …
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End of conversation
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