Real, pastured eggs or fake grain-fed eggs?
Egg consumption and mortality from colon and rec... [Nutr Cancer. 2003] - PubMed - NCBI
Egg consumption and cancer of the colon an... [Eur J Cancer Prev. 1994] - PubMed - NCBI
Eggs are such a convenient and versatile source of high quality protein, B vitamins, minerals, etc.
But maybe not such a great idea?
Real, pastured eggs or fake grain-fed eggs?
Turquoisepassion:
Knifegill is christened to be high carb now!He gives me Lamprey Kisses in the midnight seanotontherug:
the buttstuff...never interested.
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good point! probably because the vast majority of eggs consumed are from grain/hormone fed factory farmed hens
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correlation =/= causation.
I'll stick with the eggs my backyard bug-eating hens provide me.
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I'm no statistician, but that seems like a pretty serious reduction once confounds are eliminated. In fact, isn't P = 0.05 generally considered the P value threshold? Which would mean that the P values for eggs are under the threshold level.Abstract
The relation between egg consumption and mortality from colon and rectal cancers remains unclear and was investigated in this study. Colon and rectal cancer mortality data, mostly around 1993-94 and egg consumption data in nine time periods (1964-94) in 34 countries were derived from World Health Organization and Food and Agriculture Organization, respectively. Egg consumption was significantly and positively correlated with mortality from colon and rectal cancers in both sexes in most of the nine time periods. The correlations were generally stronger for colon cancer (r = 0.39 to 0.63 in men and r = 0.33 to 0.65 in women) than for rectal cancer (r = 0.18 to 0.49 in men and r = 0.08 to 0.45 in women). After adjustment for confounding factors, egg consumption was still significantly and positively associated with mortality from colon cancer in the earliest five time periods (1964-84) (P = 0.046 to 0.017 in men and P = 0.034 to 0.014 in women) and rectal cancer in the latest five time periods except for the last time period (1982-91) (P = 0.046 to 0.024 in men and P = 0.045 to 0.026 in women). This study suggested that egg consumption was associated with an increased risk of colon and rectal cancers at the population level.
And that's without considering the thoroughness of the research WRT identifying confounds.
Yes, and since the P values are less than .05 these results are statistically significant. However, all that means is that these two variables correlate positively with less than a 5% chance that it was just coincidental randomness, given the assumed mathematical parameters. That is assuming that the study thoroughly controlled for all possible conflicting variables, which is sadly rare in the tumultuous world of statistical "science" these days.
If we assume that the study is accurate, it's really only good for one thing: Justifying further investigation into the relationship of the variables with a double blind, fully controlled scientific study which isolates the variables (doing this properly can be very time consuming and expensive). It is definitely arguable that this association is strictly due hormones/antibiotics ect. as people have said, and not the eggs themselves. I'm sure this forum could point to 10 studies which show benefits of eggs for every one study like this that shows "detriments" of eggs.
So the correlation between eggs and these cancers is weak, in terms of P values.The statistical significance of a result is the probability that the observed relationship (e.g., between variables) or a difference (e.g., between means) in a sample occurred by pure chance ("luck of the draw"), and that in the population from which the sample was drawn, no such relationship or differences exist. Using less technical terms, we could say that the statistical significance of a result tells us something about the degree to which the result is "true" (in the sense of being "representative of the population").
More technically, the value of the p-value represents a decreasing index of the reliability of a result (see Brownlee, 1960). The higher the p-value, the less we can believe that the observed relation between variables in the sample is a reliable indicator of the relation between the respective variables in the population. Specifically, the p-value represents the probability of error that is involved in accepting our observed result as valid, that is, as "representative of the population." For example, a p-value of .05 (i.e.,1/20) indicates that there is a 5% probability that the relation between the variables found in our sample is a "fluke." In other words, assuming that in the population there was no relation between those variables whatsoever, and we were repeating experiments such as ours one after another, we could expect that approximately in every 20 replications of the experiment there would be one in which the relation between the variables in question would be equal or stronger than in ours. (Note that this is not the same as saying that, given that there IS a relationship between the variables, we can expect to replicate the results 5% of the time or 95% of the time; when there is a relationship between the variables in the population, the probability of replicating the study and finding that relationship is related to the statistical power of the design. See also, Power Analysis). In many areas of research, the p-value of .05 is customarily treated as a "border-line acceptable" error level.