Outlier
Bell Curve
Bell curve of normal distribution
from vlasta2's photostream
In statistical circles, I am called an outlier. When you look at a bell curve, nearly all the data is clumped toward the middle. There are a few data points far out at each end where the curve flattens. Those random data points are outliers. Those points are me. (Funny side note: a friend told me tonight that when her mom went back to school as an adult and studied statistics, she assumed “outlier” was a fancy French term and pronounced it “oot-lee-ay”.)
I have another friend with similar outlier tendencies. She walks in and tells the doctors “When you hear hooves, don’t think ‘horses’ - think ‘zebras’.” It took me a while to get it and then I laughed. Yes, most people would think that the sound of hooves meant horses approaching - not a herd of zebras. But with me, zebras are what you will get.
My friends are familiar with this phenomenon of mine. In fact, when I had my Lap-Band surgery, my friend Jen was pleasantly amazed that everything went so well. “Don’t take this wrong”, she said, “but with you, if anything can go weird, it usually will”. I was quite surprised myself. (Of course, I am apparently an outlier when it comes to the insurance end of the process, but that’s another story).
In researching a link for this post about outliers, I ran across this explanation: “An outlier is an observation that lies outside the overall pattern of a distribution. Usually, the presence of an outlier indicates some sort of problem. This can be a case which does not fit the model under study, or an error in measurement.” (emphasis mine)
So many of my medical issues “indicate some sort of problem” and are “cases which do not fit the model under study”. This is why it is so long and plodding with me – why there are so many false starts and hills climbed that end up not being very helpful.
This post started as something else and then morphed into this general observation. I have no little bon mot to close with, no tidy ending, no funny coda. I guess it’s fitting that this entry is an outlier, too.

