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A real brouhaha followed the recent wide reporting of research into the perilous effects of red meat consumption. Having been a veggie for several years in my youth my interest was well and truly piqued. The headline finding was that “eating an extra portion of red meat every day will increase your risk of death by 13% annually”. Given that, with the notable exception of earthbound deities, our risk of death is nominally considered to be 100%, the headline by itself raises some interesting issues about our use and interpretation of statistics.
But what does this actually mean?
No matter. What was particularly interesting in the reporting of this finding was what it actually means to people. Enter the dubiously titled Professor Spiegelhalter, a Cambridge University biostatistician (no, I didn’t know there were such things either) who is currently the Winton Professor of the Public Understanding of Risk (ditto). He said to imagine two versions of yourself at 40, one who eats just 3 ounces (85g) of red meat a day, and one who eats 6 ounces (170g). The second version of you would die at 79, the former at 80. Cue a big sigh of relief from the committed carnivores amongst us, for whom this difference seems rather small, and not nearly as important as a 13% increase in risk of death! But before breathing a collective sigh of relief and flossing the bits of meat from between their teeth, Prof Spiegelhalter suggests the committed meat eaters could instead view it as each time they are eat their extra daily hamburger, they are actually shortening their lives by more than half an hour! Suddenly it seems a bit more serious...
The way we frame data is all important
This is a very clear demonstration of the importance of how we, in the research and analytics business, not just analyse data, but how we frame it. We have probably all been in meetings discussing not just the statistical but practical significance of a 0.4 mean increase in customer satisfaction on a 10 point scale. What does it actually mean to the client? Is it enough to poach customers, drive loyalty, or differentiate their offering? The illustrations put forward by Prof. Spiegelhalter remind us of the power of framing data in a meaningful way.
Another debate surfaced again last week – that of the potential benefits versus risks associated with aspirin. Advocates portray it as a ‘wonder drug’, with continued low dosage drastically reducing risk of heart disease and cancer. Others, however, point to increased risks of internal bleeding as outweighing these potential benefits. During a considered, extensive (and very dry) debate on the latest aspirin research to be published in The Lancet, the research’s author repeatedly demurred as to whether people should take a daily aspirin. He suggested instead they ask their doctor. Finally the interviewer said “The people listening to this interview will want to know what you do. Do you take a daily aspirin?” To which he replied simply, “Yes”. And then there was closure – a clear signal on what the data was telling us, and what it actually means for people. The data and subtle points of argument were distilled into a simple question with a decisive answer. Again, there are clear parallels with how we (in the research and analytics business) should approach our own communication of research findings.
So, taken together with last week’s advice from the Dalai Lama, for a healthy and happy life, it seems that we should be compassionate to others, only eat a little red meat, and take a daily low dose aspirin (if we are over 40 and in a high risk group for heart disease or cancer). Oh, and something that hasn’t been so widely reported – floss every day. It’ll add years to your life. Really.