THE OVERSIZED POWER OF SMALL DATA


are in similar places on both plots, meaning that the estimated values are similar from both the good and bad experiments.


Values within the ranges of the bars are those that are reasonably believable based on the data. These ranges represent the uncertainty in our conclusions regarding how far Bert and Ernie push the box on average. Notice there is much more uncertainty in the plot on the right from the poorly designed experiment. Using the plot from the well-designed experiment on the left, we can conclude with little risk that Ernie is pushing harder than Bert.


This increase in uncertainty is a result of the ways the tests were planned. The increase is moderate compared to what can easily occur. Because of the increased uncertainty, there will be more risk involved for any decision that depends on under- standing how hard each of them pushes.


In the sensor example, the way the test was modi- fied caused uncertainty to increase so much that we could not form any meaningful conclusions about how the error or several of the other test variables affected the ability of the vehicle to avoid the sensor.


Changing the sensor test appears to have been unwarranted, and the way it was changed increased the uncertainty in our conclusions to the extent that they were not useful.


—JASON MARTIN


analyze the data, report results and make a decision without ever knowing mistakes were made. Such decisions are built on a house of cards that can be costly in terms of dollars, time or even lives.


A SYSTEMIC SOLUTION Te hole in our small data capabilities also presents a tremendous opportunity. For each of the thousands of small data decisions we make, we can learn to use statistical methods that help ensure that we 1) spend appropriate resources to collect the right amount of data, 2) collect the right data to most fully answer our ques- tions and 3) perform analysis that most accurately quantifies what we believe in a way that communicates the uncertainty in conclusions. Tis will fundamentally change our abilities to most effectively use resources and take calculated risks.


QUESTIONS FOR LEADERSHIP I know from experience that widespread adoption of the statis- tical methods we need is not likely to happen without strong leadership. Decision-makers must encourage it by asking the right questions and insisting that we use rigorous statistical processes to create and analyze data. We need leaders and decision-makers to know which questions to ask and how to recognize an adequate answer.


Here are examples of some important questions and information we should always know. Te answers should be based on rigor- ous statistical methods, not opinions.


• Is that test the right size? Do we need more or fewer test runs? What assumptions were made to determine the size of the test and why? Please show me the (simple) results of calculations that support the plan.


EXPANDING THE CIRCLE


Small data is the basis for many, if not most, acquisition decisions. The Naval Postgraduate School recognizes the importance of data science education for DOD, and it has launched an interdisciplinary Data Science and Analytics Group, which will provide better education, research programs and advisory services to DOD. (Photo by Matthew Schehl, Naval Postgraduate School)


90


Army AL&T Magazine


Summer 2021


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