THE OVERSIZED POWER OF SMALL DATA
decisions, small data from developmental and operational tests most likely will be used. Small data probably would be used in designing the system, as well. Both big and small data are important.
THE BEST LAID PLANS… Here’s a real-world example demonstrat- ing the challenges of small data and what can happen if we don’t understand it. I worked on a test in which we measured whether a vehicle could avoid detection by a sensor. If it did, then we recorded a success. If not, a failure.
During the test, we systematically controlled five variables to determine their effect on the ability of the vehicle to avoid detection. Te variables included distance from sensor to the vehicle, speed of the vehicle, the aspect of the vehicle relative to the sensor, etc.
Te test plan we developed involved a few hundred data points that would enable us to learn not only the effects of the five variables, but also effects of all two-way combinations of the five variables. For example, we didn’t just want to under- stand how distance affected detection; we wanted to understand how the effect of distance changed as speed changed.
This is one of the underappreciated
challenges of small data. Te effects of combinations of variables are often impor- tant, and likely will become even more important as the systems we develop become more complex. Experiments must be planned appropriately to ensure we are able to understand these combinations, or interactions.
While it may take a little time to grasp the concept of an interaction fully (it did for me), they are very common. If you're familiar with baking, you know that, to a point, adding salt can enhance other
flavors. Tis is an example of an interac- tion between salt and another ingredient; the effects of another ingredient on flavor depend on how much salt is added. More generally, in a scientific formula, every time you see two or more values multi- plied or divided, that’s an interaction. Tough common, interactions often are not considered when planning a test and analyzing data, and we learn less than we can if they are considered.
second mistake in the way the test was modified.
Unfortunately, the two mistakes prevented us from understanding whether the “error” was actually an improvement and, worse, from understanding the effects of the orig- inal five variables, the original goal of the test. I should mention that the engineers who made these mistakes are excellent engineers, among the best in their field. If they can make these mistakes, any of us can.
We tend to think that small amounts of data tell us more about future events than they do.
…CAN GO AWRY After the test was complete, I was told that our original plan was modified midway through the test. An operator made an error when executing a move to avoid the sensor, and the vehicle avoided detection (a success) when it wasn’t expected to. So the same “error” was tried again, and the result was another success. After a dozen or more trials with the same “error,” there were more successes than experts expected, so they concluded that the change most likely improved the ability of the object to avoid detection. Tis was the first mistake.
Te rest of the test was altered to include the change with the expectation of more thoroughly demonstrating an important improvement. While changing a test is frequently necessary, engineers made a
WHAT HAPPENED? How could so much go wrong when some excellent engineers made a seemingly simple change to a test? Working with small data has challenges that are often underappreciated, and the testers weren’t aware of two common challenges and how to address them. It is important to point out that while this example involves modi- fying a test, these challenges are equally relevant when initially planning a test and the same mistakes are often made at that time.
Te first challenge is knowing how much data is necessary to make a decision. Tough we can’t know for sure for reasons that will be explained below, it appears the unexpected successes were not a result of the “error,” but just random occurrences. Tis is the same kind of randomness that allows you to get eight heads when you toss a coin 10 times and then get four heads in the next 10 tosses. It was as if the vehicle went on a lucky streak and avoided the sensor more times than expected, but the streak was mistakenly attributed to the “error.” Te decision to change the test was made without enough data to distinguish a lucky streak from something meaningful. To avoid such mistakes we have to collect enough data (but not too much, that’s overly costly) to allow us to determine with acceptable risk whether something
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Army AL&T Magazine
Summer 2021
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