In the world, and everything that concerns our history, human beings have lived for a long time involved among systems of measurement. And primitive forms of measurement not only retained this empirical characteristic, but evolved into functional systems.
Systems of measurement represent tradition and human culture, elements that are still perceived. It was, however, only a matter of time before technology permeated every trace of this ancient resource and its chances of evolution, transformation and improvement were greatly increased.
For us, metrology plays a much more important role in our lives, although it is easily overlooked.
What is the role of metrology?
In today’s society, full of technology, politics, economics and other aspects of daily life, metrology may seem like a forgotten science, but in reality it is more widespread than ever.
Measurements are of great and vital importance in the fields of research and science. They are carried out to increase skills, such as acquiring new knowledge, creating systems for commercial, industrial, technological purposes and many more things. Today, global trade has intensified tremendously, 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 subject to a breathalyzer, if you buy shoes, clothes, food, household appliances, etc.
Understanding how the science of measurement can be involved in so many aspects of everyday life can be complex at first, but the more you learn about it, the more you can admire and learn from it. However, not everything will be as easy as we think, because in any 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, measurement error and measurement uncertainty have almost perfect parallelism, because they join a very fine 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.
Measurement error
In the same vein of inaccuracy of statistics, there is no one that is absolute, and 100% is not reliable, because there will always be an incidence that generates a variable in the accounts. The same goes for metrology and its errors: no measurement and its reading is entirely accurate, and it will never be repeated to give the same result, regardless of whether the measurement is made by the same person, with the same instrument, on the same part and applying the same method.
A measurement error has two formats of occurrence: systematic error and random error.
Systematic error
A systematic measurement error is an error that can be reduced or corrected, if its exact causes are discovered. This type of error is predictable, through a series of repeated measurements, so that an analysis of the occurrence and readings is traced to detect the specific cause.
Random error
On the other hand, random measurement error promotes unpredictable behaviors, and it is not practical to act on them in the same way as systematic error. Its unpredictable appearance generates varied readings, due to temporal and spatial variations in temperature, humidity, pressure, etc. They are not correctable, but the dispersion around the average value can be reduced.
In other words, an error is the difference between a measured value and the true, conventional value of the measured object.
Measurement uncertainty
Unlike errors, measurement uncertainty is the quantification of doubt, which is obtained from the result of a measurement. The uncertainty value lists the components arising from systematic and random effects on previous measurements, due to elements that are calculated by a series of statistical distributions, from 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 a measurement 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.
Thus, an error and an uncertainty differ, in the sense that the error is the representation of the difference between a measured value of a quantity and a reference value, and that the uncertainty quantitatively evaluates the quality of a result of measurement, by a standard deviation.
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