Normal 0 false false false EN-US X-NONE X-NONE The average and standard deviation of, say, strength or dimensional test data are basic engineering and scientific math, simple to calculate.What those resulting values actually mean, though, may not be simple - and can be surprisingly different from what a researcher intends to calculate and communicate. Mistakes can lead to over- or under-reporting of analysis values, overlarge estimates of spread and other challenges to understanding or communicating what your data is really saying.
This talk will discuss some common errors and missed opportunities seen in engineering and scientific analyses along with mitigations that can be applied through smart and efficient test planning and analysis. It will cover when - and when not - to report a simple mean of a dataset based on the way the data was taken; why ignoring this often either hides or overstates risk; and a standard method for planning tests and analyses to avoid this problem. And it will cover what investigators can correctly (or incorrectly) say about means and standard deviations of data, including how and why to describe uncertainty and assumptions depending on what a value will be used for.
This presentation is geared toward the analyst or even manager charged with test planning, data analysis or understanding findings from tests and other analyses. Attenders' basic understanding of quantitative data analysis is recommended; more-experienced participants will grasp correspondingly more nuance from the pitch. Some knowledge of statistics is helpful. Participants will be challenged to think about an average as not just "the average", but as a valuable number that can and must relate to the real problem to be solved that must be firmly based in the data. Attenders will leave the talk with a more sophisticated understanding of this basic, ubiquitous but surprisingly nuanced statistic and greater appreciation of its power as an engineering tool.
(Note to abstract review committee: this talk is expected to be presented at the 2019 NASA-DoD DATAWorks workshop.)
- : Kenneth L. Johnson
- : NASA Engineering and Safety Center (NESC)
- : Kenneth L. (Ken) Johnson
- : tutorial/case_study
- : introductory/practitioner
- : email@example.com
- : 256-698-1025
Your Mean May Not Mean What You Mean It to Mean