The question really comes down to whether increasing the sample size of a study reduces the biases in your data collection Let me explainpic.twitter.com/NZHBBEN1wu
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So let's go back to our original two nutritional epidemiology studies. Both were huge (n=50,000 and 500,000) BUT they had very different populationspic.twitter.com/XP7HlGlHrc
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The recent study looked at a group of people living in North America. The older study was on adults living in China These are very different
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We can't expect the results of Chinese people, even if the sample size is HUGE, to directly generalize to people living in the USA
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These two regions of the world have vastly different food intakes, very different cultures, and this means that egg eating has different meanings and connotations in both placespic.twitter.com/kXSP5emQIC
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This was actually central to the hypothesis of the Chinese study. The researchers argued that Chinese eating habits were different, and so the egg consumption should be studied to see if it was beneficial despite previous studies in other populations showing harmpic.twitter.com/Weto2IJUv1
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In fact, while the two studies APPEAR contradictory, it's likely that this is, in part, context-based Chinese people who eat more eggs are healthier Americans who eat more eggs are less healthy BOTH OF THESE THINGS CAN BE TRUEpic.twitter.com/x5x57XLrkG
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TL:DR - two studies appear contradictory (eggs good or bad?) Realistically, it's likely just the differences between the settings for the studies Bottom line, bigger studies are not always better it's more complicated than that
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P.s. the REASON that egg eating is associated with good or bad health is, in my opinion, more complex than either of these studies suggests
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My personal argument is that it's ~almost certain~ that both of these studies are using egg eating as a marker for some sort of social influence that is really causing the heart disease
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So people in the US who eat more eggs are probably less well off in many ways, with the inverse being true in China
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End of conversation
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