Bits and Bobs —
Happy Friday, everyone.
Here in St. Louis, everyone is in a great mood because some sort of sporting event has ended well for the time being, and our civic egos have remained in tact for another day. Wish us luck!
This has also been a very exciting week for Fierce, Freethinking Fatties, as we have introduced four new bloggers to our ranks, including Gabriela, Bree, Heather, and Bronwen. You can check out their micro-bios here.
And on top of all that, we have the pleasure of celebrating the marriage of our very own erylin to her long-time love monkey, Bob. Congratulations and best wishes to them as they begin this new chapter in their family, which will most likely look extraordinarily similar to the old chapters filled with the same happiness and joy, heartache and struggles, that they’ve lived through and loved through all this time. May their future be even brighter than the love they have shared thus far.
All that being said, I wanted to share two items that I’ve recently observed and found interesting.
First, last week on Love Your Body Day, I shared my struggle to accept the parts of my body that I’m not so wild about, including my squinty eyes. At the time, I explained how the genetic thread that runs from me to my Grandma Kate* is one of the reasons why I have been able to see my eyes (pun intended) in a new light.
Well, yesterday was Linny’s fifth birthday and as we were looking through photos to post on Facebook, I came across one that further reinforced the power and pride I have in my genetic inheritance for better or worse. When we first got our cats, Lola and Puff, they took some time to explore the house and even found a tiny new home to enjoy. This delighted Linny so much that her genetic inheritance overtook her face.
I see this photo and think how beautifully happy she is, and yet this is how I look when I am overwhelmed with joy, as in the day this photo was taken at my cousin’s wedding.
And finally, as you all know, I love digging into research. I currently have a thick-ass stack of research in my computer back that I plow through on the train both to and from work. I’ve found a lot of interesting studies this way and have gotten fairly good at determining what studies are actually saying.
However, one of my weaknesses is that I currently lack the mathematical grasp to determine the quality of studies, so that even if a study says, “Fat people are going to die NOW!” I can’t tell if the data they use is statistically meaningful or blown out of proportion. I simply have to trust that the peer review process has prevented crap from getting through, which isn’t always the case.
While at my local library a few weeks back, I asked for help in finding a book on reading research to improve my grasp the mathetmatical principles behind statistics and the only one they had was this 1967 college textbook.
I am probably one of the least mathematically-inclined people on the planet (I never got my high school diploma because my senior year I failed Honors Calculus), so I was relieved when I read this blurb on the back:
This concise yet comprehensive introduction to statistics is designed to be not only a guide for students of statistics but also an invaluable companion to the non-mathematician who needs statistics for work in economics and the social sciences, psychology or business.
That’s me! I’m a non-mathematician!
So, I dug into the book heartily and found it instructive and intriguing… until I realized that the blurb was bullshit and that this book was chock full of mathematical gobbledygook.
Therefore, I have broken down and requested a copy of Statistics for Dummies from the library, admitting that I am a mathematical dummy.
However, before I surrendered my status as “non-dummy,” I found this golden passage in the Basic Concepts chapter that should be familiar to all of you. And keep in mind that the emphasis was put in by the book’s author, Boris Parl, because of the importance of the concept:
Got that, Obesity Researchers, Media and Physicians everywhere? Since at least 1967, it has been considered a basic concept of statistics that correlation does not equal causation and that stacking up a bunch of impressive tables does not prove your panic-driven warnings of obesity doom. You can’t just show that since there is a relationship between obesity and hypertension, diabetes, or bad cholesterol does not prove that obesity causes these same diseases.
So, thank you Boris Parl for this enlightening us once again as to the role statistics plays in etiological studies.
Now, if you don’t mind, I have some coloring books to attend to.