COMMENTARY
happens for an identifiable reason or just randomly.
Te second challenge is planning or modi- fying a test in a way that allows us to understand clearly the effect of each vari- able on the result we are measuring. We wanted to understand how five variables, including their two-way interactions, affected the ability of a vehicle to avoid a sensor. In the middle of the test, we added the goal of understanding whether the “error” improved this ability.
While it is sometimes necessary to modify a test, this one was changed in a way that confounded the “error” with some of the other controlled variables, so we couldn’t tell what variables actually affected the vehicle’s ability to avoid detection. Te people who made the change had no idea this had happened. Te sidebar on Page 88, "Unforced Error," provides a simple example to explain what happens when a test is designed or modified incorrectly.
A SYSTEMIC PROBLEM Small data decisions are difficult
for
nearly everyone. In his book "Tinking, Fast and Slow," Nobel Prize laureate, psychologist and behavioral economist Daniel Kahneman discusses the weak- ness humans have in our intuition about small data. We tend to think that small amounts of data tell us more about future events than they do. Tis appears to be what happened when engineers believed the “error” had an impact of the ability of the vehicle to avoid the sensor. Intuitions about the meaning of the data they collected failed them, and they didn’t know the statistical methods that would help them avoid the mistake.
The engineers made mistakes when modifying the test because they didn’t understand the necessity or the use of statistical methods, known as design of
TURBO TESTING
How much data is enough? That’s one of the first questions answered by test designers for the Infantry Squad Vehicle, currently being evaluated at U.S. Army Yuma Proving Ground, Arizona. Powered by a 2.8-liter turbo diesel engine with a six-speed automatic transmission, the four-wheel-drive vehicle carries up to nine Soldiers and their gear. (Photo by Mark Schauer, U.S. Army Yuma Proving Ground)
experiments, that were needed to modify (and initially create) the test correctly. Tey neither realized the need nor knew how to make sure Bert and Ernie push from adjacent sides of the box.
Te mistakes happened because, through no fault of their own, some excellent engineers did not understand a few fundamental statistical concepts.
It is tempting to think that engineers and scientists who are good with numbers are also naturally good at collecting and analyzing data. Tis is not true. Creating the right data to help answer a question and analyzing it in the most informa- tive way requires an understanding of statistical methods that allow us to deal effectively with randomness and uncer- tainty. Intuition is completely insufficient.
While the Army certainly has individu- als and groups with expertise in working with small data, mistakes with small data are systemic and partly a result of defi- ciencies in engineering and scientific curriculums. Most college graduates in sciences and engineering arrive in the workforce with an understanding of equations and theories, but with limited skills to deal with the random variabil- ity inherent in the real world. Statistical methods allow us to cope with this vari- ability when deciding what data to create, when analyzing the data to develop useful information and when making decisions. Even those with academic training often struggle with practical application for complex military applications. Tough available, few people receive on-the-job training in effective use of statistical methods.
https://asc.ar my.mil
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