Here’s a really juicy and intriguing question for folks like me (and, by association, you). Folks who are hungry for the latest ‘facts’ about nutrition and health. And there’s nothing we enjoy more than a new learned study…
Red Wine: Another topic that has generated years worth of back-and-forth
‘scientific’ conclusions about it’s relative merits and dangers to health.
Take eggs and cardio health
Any reader of this blog for any length of time will know that, the bigger the topic, the more schools of thought there are about its whys and wherefores. One really good example is the long-debated connection between eating eggs and heart disease. For years, I’ve reported successive experimental findings – by allegedly smart, reliable well-educated and highly trained thinkers – that have contradicted each other, one after another, time after time.
The messages delivered by these official sounding ‘study reports’ deliver results and recommendato0ns across an astoundingly board spectruum. Some say, “Eggs are bad. Cut them to a minimum! Others insist: “There’s no reason to restrict egg intake!” More and more these days have come full circle on the issue, counselling: “No reason not to eat as eggs as often as once a day!” And, “Eggs’ dietary benefits outweigh the drawbacks!”
What’s an average person to believe?
Not the statistics, according to the latest critical look at traditional data-mining techniques and practices.
The research, led by scientists at the University of Leeds and The Alan Turing Institute – The National Institute for data science and artificial intelligence – reveals that the standard and most common statistical approach to studying the relationship between food and health can give misleading and meaningless results.
“These findings are relevant to everything we think we know about the effect of food on health,” says Lead author Georgia Tomova. And there are many, many factors that vary from study to study that tend to pollute the results.
Why the persistent misleading messages?
“Because of the big differences between individual studies, we tend to rely on review articles to provide an average estimate of whether, and to what extent, a particular food causes a particular health condition,” Tomova points out. “Unfortunately, because most studies have different approaches to controlling for the rest of the diet, it is likely that each study is estimating a very different quantity, making the ‘average’ rather meaningless.”
Senior author of today’s focus, the study on the use of statistics, Dr. Peter Tennant explains: “When you cannot run an experiment, it is very difficult to determine whether, and to what extent, something causes something else. […] That is why [those writing reports on statistics-based science] tend to use disclaimers such as, ‘correlation does not equal causation.’ These new ‘causal inference’ methods promise to help us to identify causal effects from correlations, but in doing so they have also highlighted quite a few areas which we did not fully understand.”
The final word…
Which leads naturally (and inexorably) to the same inconclusive conclusion that many less-than-self-confident, in an abundance of caution, tend to fall back on: “More research is needed in this area before we draw hard and fast conclusions…”
Muse on that!
~ Maggie J.