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Jeff Bezos famously said "When the data and the anecdotes disagree, the anecdotes are usually right."
This is dead on, based on my experiences. Data can be manipulated in so many ways - from what is actually measured, to how the measurement is interpreted, to whether the measurement is shared - it's all subject to manipulation.
The example that Jeff Bezos gave was customer support call wait times at Amazon.
One notable example Bezos shared involved Amazon's customer service wait times. While internal metrics suggested customers waited less than 60 seconds, anecdotal feedback indicated otherwise. To prove his point, Bezos called Amazon's customer service during a meeting and demonstrated that the wait time was significantly longer than the data suggested.
My experiences with customer support wait times are that often, the metric used (to assess whether the customer support agent is efficient) is call time. A long call time is bad, a short call time is good.
So what do the agents do? Hang up on people. That reduces their average call time.
Another item - measuring how many school days there are in a year, on the assumption that more school days are better. I don't actually believe that more school days are better, but even that metric is flawed, in the sense that - what are counted as school days are frequently NOT actually school days. For instance, in some public schools in the US, if one grade is taking a test such as the SAT, the other grades stay home. However - this still counts as a school day, for all students!
Another one - when "serving" the homeless, lots of organizations promote metrics such as the number of "comfort packages" given out (it'll be something like a package with blanket, socks, soap, toothbrush, etc). That's a completely useless metric, if it's supposed to be measuring whether people are actually helped. All you end up with is a bunch of blankets and other donated goods, littering the ground wherever the homeless congregate.
How about you folks - what "anecdote vs data" examples do you have?
This is related to Goodhart's Law: "When a measure becomes a target it ceases to to be a good measure."
It also highlights the high level of expertise necessary to interpret data correctly. Anecdotes are not always better than data, but it takes a lot of skill and experience to interpret data correctly--and more often than not the data is abused instead of being used correctly.
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And the experts are usually part of the system, so they often have a vested interest in keeping things the way they are...
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Exactly... the ones who are qualified to interpret the data accurately are often the least incentivized to interpret it accurately.
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This extends towards "rule of thumb". And it's funny because it's a recursive reference, since the statement is also a "rule of thumb".
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It's a good point, but probably most highlights that people will tell you (with numbers or analogies) what they want to say, not necessarily the truth.
It's possible that an anecdote is easier to verify than a large scale statistical study.
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Been there. Worked at a place where no emoji click after support chat = automatically satisfied customer. CSAT scores looked amazing while actual service was trash. Productivity metrics are the same scam. Track tickets closed, people game it with easy wins. Track mouse movement, people buy jigglers. Track keystrokes, people find workarounds. Management celebrates efficiency while real work suffers. The disconnect between what gets measured and what actually matters is real. The people doing the actual job usually know when the numbers are bullshit. Anecdotes will capture what sanitized metrics miss.
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Yes! I had a friend who worked remotely, who would always have one of her kids go "jiggle" the mouse every once in a while, so she seemed active. And then of course, she got a "jiggler".
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School days doesn't let someone do a spare activity as it is plenty full time of study in between.
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