The Perils of Arbitrary and False Precision

[Ken White]

...
I batted 50% with those four, one went on to be the hero of New Orleans, the other the hero of Mosul. A coincidence perhaps, but I think crises helps focus the mind.

Of Ol' Russ is deserving of accolades.

Or many days resting and loafing in the shade...

[Jedburgh]

Edge, 15 Sep 08: The Fourth Quadrant: A Map of the Limits of Statistics

......In the following Edge original essay, Taleb continues his examination of Black Swans, the highly improbable and unpredictable events that have massive impact. He claims that those who are putting society at risk are "no true statisticians", merely people using statistics either without understanding them, or in a self-serving manner. "The current subprime crisis did wonders to help me drill my point about the limits of statistically driven claims," he says.

Taleb, looking at the cataclysmic situation facing financial institutions today, points out that "the banking system, betting against Black Swans, has lost over 1 Trillion dollars (so far), more than was ever made in the history of banking".

But, as he points out, there is also good news.

We can identify where the danger zone is located, which I call "the fourth quadrant", and show it on a map with more or less clear boundaries. A map is a useful thing because you know where you are safe and where your knowledge is questionable. So I drew for the Edge readers a tableau showing the boundaries where statistics works well and where it is questionable or unreliable. Now once you identify where the danger zone is, where your knowledge is no longer valid, you can easily make some policy rules: how to conduct yourself in that fourth quadrant; what to avoid......

[Van]

The confusion between accuracy and precision is one of my personal hot-buttons. As an example of the problem...

A guy is measuring things (doesn't really matter what) and only has tools to measure to a tenth of an increment (1.2). He starts talking about 80% of this quantity, and insists that it is exactly .96 (1.2 x .8), without realizing that this is stupid, as he can't measure to this degree of precision, where describing it as "between .9 and 1.0" makes sense and is accurate.

Another example;
A guy with a watch that is only marked 1 through 12 without hashmarks for minutes can be on time every time for appointments because his watch is accurate, gains or loses only a couple of seconds a day, and reflects the time on Naval Observatory's atomic clock, but without precision. The guy with the display down to seconds is always late because his watch loses two minutes a day, or possess greater precision by two orders of magnitude without being accurate.

But words like "about" and ending in "-ish" disturb the harmony of the Type A personalities that call the shots, so they demand a precise and wrong number rather than a less precise but accurate number.

And this is why I argued that CAS3 should include a block on statistics (and got told to sit down and shut up).

[120mm]

But words like "about" and ending in "-ish" disturb the harmony of the Type A personalities that call the shots, so they demand a precise and wrong number rather than a less precise but accurate number.

Especially the mouth-breaking jerkoffs who try to make you come up with a binding number on the spot, in a scene reminiscent of "A Few Good Men".

Dude, I just told you my timeline was completely dependent on a house of cards. How the heck do you expect me to make a time estimate that you will tie me to, later....

[Ron Humphrey]

And this is why I argued that CAS3 should include a block on statistics (and got told to sit down and shut up).

My Stats course did do a lot for me in terms of remembering to take metrics with the BIIIIIG bucket of salt they require

And I really liked your analogy considering that although the extreme precision watch is problematic it doesnt change the fact that the regular watch is still a ton better than trying to guess what time it is by looking to the sky(especially at night)

Metric use -Good
Metric abuse -bad
Metric Dependancy- Requires a lot of therapy and tons of CS(Common Sense) checks in order to be addressed

[selil]

In my area we deal with accuracy versus reliability especially with assessments. Lots of pictures of arrows on targets which I really understand.

[Ron Humphrey]

In my area we deal with accuracy versus reliability especially with assessments. Lots of pictures of arrows on targets which I really understand.

I think the biggest thing that bothers me is how useful metrics can be used correctly and in context with the purpose for which they are being used.

And how more often than not this does not end up being the case due to a variety of things not the least of which is ignorance of what some are actually trying to use them for.

Accuracy has always seemed rather interesting to me in that we actually tend to believe we are creating an accurate picture or understanding of something yet when it all comes down to ground truth we are suddenly amazed by how little our "accurate" assesments actually got right.

I propose that if whatever one is working with or through is not both
(Reliably Accurate) than the greatest part of what we end up with will continue to be right only about 50 percent of the time.


This is one reason I am a strong supporter of trendal analysis because although it uses most of the same metrics you know before you even start that what you end up will not be answers but rather guidelines/possibilities.

So you always get what you expected No more, No Less

[selil]

When I am assessing the amount of information that students have ended up with in a class (based on all of the learning methods utilized and all experiences in and out of class) and based on the learning literacy of the assessment I can find nice gaussian bell curves. Some students will be bulls eyes with accuracy and reliability. Some students will be reliably wrong. Some will hit all over the target. If students who are otherwise reliable and accurate get something wrong I look to see if it was graded wrong or I taught it wrong. That is how I use accuracy and reliability.

Unfortunately the bell curve has limited utility in much of my research. I deal with binary data that the outliers are the important element. Averages have little in relationship to the rest of the environment. Myself I don't believe very much in trend analysis or other predictive methods. Only that which can be observed. Sure we all do it and it is fun, but prediction even with high reliability is rarely scientific.