In the world, and all that concerns our history, the human being has lived involved in measurement systems for a long time. And primitive forms of measurement not only maintained that empirical characteristic, but evolved to become functional systems.
Measurement systems represent tradition and human culture, elements that are still perceived. It was only a matter of time, however, before technology permeated every trace of this ancestral resource, and its chances of evolution, transformation and improvement were increased completely.
For us, metrology plays a much more important role in our lives, despite being easily overlooked.
What role does metrology play?
In today’s society, full of technology, politics, economics and other aspects of everyday life, metrology may seem like a forgotten science that is actually more prevalent than ever before.
Measurements are of great and vital importance in the fields of research and science. These are performed to increase skills, such as gaining new knowledge, creating systems for commercial, industrial, technological purposes and much more. Today, global trade has escalated formidably, and the implementation of metrology has also reached memorable degrees of importance, as it works for the perfect relationship between measurement and quality control, calibration, laboratory accreditation, traceability and certification.
The science that represents metrology can be found if you wake up in the morning and look at the clock, if you drive and check the speed of the vehicle, if you have to pay a fine, if you are subjected to a breathalyzer test, if you buy shoes, clothes, food, household appliances, and so on.
Understanding how the science of measurement can be involved in so many everyday aspects of life can be complex at first, but the more you learn about it, the more you will be able to admire it and learn from it. However, not everything will be as easy as we think, because in every existing system there is room for errors, and metrology has twice as many possibilities: errors and uncertainties.
Error and uncertainty: what are they and how do they differ?
Generally speaking, a measurement error and a measurement uncertainty have an almost perfect parallelism, as they join a very thin theoretical and practical line. However, fully understanding what defines them individually will mark a turning point in your quest to know what they are and what they represent in metrology.
Following the same line of inaccuracy of statistics, there is not one that is absolute, and 100% is unreliable, since there will always be an incidence that generates a variable in the accounts. The same happens with metrology and its errors: no measurement and its reading is entirely accurate, and it will never be repeated yielding the same result, no matter if the measurement is made by the same person, with the same instrument, on the same piece and applying the same method.
A measurement error has two formats of occurrence: systematic error and random error.
A systematic measurement error is one that can be reduced or corrected, if its exact causes are discovered. This type of error is predictable, due to a series of repeated measurements, so that an analysis of occurrence and readings is traced to detect the specific cause.
On the other hand, a random measurement error promotes unpredictable behavior, and it is not convenient to act on them in the same way as a systematic error. Its unpredictable aspect generates varied readings, due to temporal and spatial variations in temperature, humidity, pressure and more. They are not correctable, but the scatter around the mean value can be reduced.
In other words, an error is the difference between a measured value and the true conventional value of the object being measured.
Unlike errors, measurement uncertainty is the quantification of the doubt, which is obtained from the result of a measurement. The uncertainty value has the list of components coming from systematic and random effects on previous measurements, due to elements that are calculated by a series of statistical distributions, of the measurement values.
These uncertainties can be divided into two components:
Uncertainty TYPE A
These measurements are those that are evaluated by statistical methods, which yield measurements represented by a typical transfer (these typical transfers are represented by the symbol UI).
Uncertainty TYPE B
The TYPE B measurement is one that is evaluated by other methods. It is based on laboratory work, where all relevant information is available, which may include previous measurements, experience, knowledge of behavior and more.
Therefore, an error and an uncertainty differ, in that the error is the representation of the difference between a measured value of a quantity and a reference value, and the uncertainty quantitatively evaluates the quality of the result of a measurement, by a standard deviation.
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