Greg Rolfe in Directors and Executives, Engineers and Technicians, Administrative Pastor • Beacon Hill. Baptist Church Feb 23, 2021 · 1 min read · 1.5K

Trouble with statistics

For what feels like the majority of my life I have been bombarded with statistics. A set of numbers intended to inform and direct my attention. From the ever-popular 9 out of 10 doctors to the more clinical 30% of the last 43%. You know of what I speak.

But the problem is clear, just how accurate are these numbers? The more you actually look at statistics the more you come to understand just how flexible they are. The smaller the sample rate the greater the potential error, then again the demographic affects the results significantly if you are pooling. The list goes on and on. So just what value does this concept hold?

I have spent my life referring to these numbers as if they were facts or true metrics upon which I could adjust or base decisions on. When in fact they are apparently nothing but marketing tools. This frustrating realization is tantamount to finding out that the tooth fairy isn't real. Who brought me the ever-coveted coins when I left my tooth under my pillow? Now to find out my numbers are just as hypothetical.

Oh, sad day indeed. But now I find myself grown looking once again for that mythical fairy, that warm blanket of numbers. My grasping hands finding instead marketing ploys and guesses. 50 percent of all marriages fail, True? How about that new reality that more and more couples are choosing to ignore the marriage vows altogether. Was that based on the previous statistic? Was that statistic ever accurate?

The problem with statistics is that regardless of the value of the work behind them they are intended to affect our decisions. Statistics are intended to guide our thinking and motivate our choices. But just how valuable are they really. The more I study them more I find that they are just as true as my good friend the tooth fairy.

Please someone tell me I am wrong. I enjoyed my warm blanket.


Trouble with statistics

Ken Boddie Feb 26, 2021 · #18

#14 Again, what’s with the insults, Zack? People have different opinions based on different experiences. The only thing I have to ponder is my disappointment that you have chosen to turn this into a personal attack.

Greg Rolfe Feb 25, 2021 · #17

@Ken Boddie and @Zacharias 🐝 Voulgaris thank you both for your positions! My background agrees with Ken while my frustration agrees with Zacharias. Thank you again for adding context to this discussion.

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Greg Rolfe Feb 25, 2021 · #16

#15 @Pascal Derrien Dude!!! Sweet!

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Pascal Derrien Feb 25, 2021 · #15

numbers as a mean rather than a finality but I honestly prefer words I find them warmer a blanket :-)

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Zacharias 🐝 Voulgaris Feb 25, 2021 · #14

#13 So, your own experience in a very specific field trumps the meta-analysis of an independent researcher? With such a shallow understanding of generalization, no wonder you advocate the merits of this field! If Stats is so great at adding value, why is everyone shifting towards data-driven approaches to analytics, such as Machine Learning and A.I.? Something to ponder upon.

Ken Boddie Feb 25, 2021 · #13

#12 I can only assume from your comment, Zack, that you do not understand how statistic is used to good effect in civil/geotechnical engineering design. My view is based on decades of reproducible work well documented in design procedures and published texts. As it appears to differ from your viewpoint and doubtless from your experience in a different environment, please do not make the assumption that my opinion would benefit from improved accuracy.

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Zacharias 🐝 Voulgaris Feb 25, 2021 · #12

@Ken Boddie, perhaps Prof. Ioannides' paper on the failure of reproducible results in scientific papers will help you get a more accurate view of this field and how it has failed science repeatedly. In fact, I'd go so far as to say that science would be better off without Stats, at this point, OR with a new more data-driven framework (maybe Stats 2.0) that takes into account reality more than mathematical models. Cheers

Ken Boddie Feb 25, 2021 · #11

I trust, Greg, that my comments in #9 & 10 below may reinstate your faith in statistics, at least when used earnestly and appropriately in legitimate design. 🤗

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