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Uncertainty in Measurement: Accuracy and Precision03:37

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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Total error vs. measurement uncertainty: revolution or evolution?

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    Analytical performance goals in clinical chemistry are debated. The "total error" theory, while influential, needs critical discussion alongside uncertainty theory for better medical diagnostics.

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    Area of Science:

    • Clinical Chemistry
    • Metrology
    • Analytical Performance Goals

    Background:

    • The 2014 Milan EFLM conference reviewed analytical performance goals, maintaining the Stockholm 1999 hierarchy.
    • A critical overview of performance goals and
    • total error
    • theory by Jim Westgard followed the Milan conference.
    • The
    • total error
    • theory dominates clinical chemistry but faces challenges in broader metrology.

    Purpose of the Study:

    • To debate the pros and cons of the
    • total error
    • theory in clinical chemistry.
    • To explore methods incorporating all uncertainty causes for medical diagnoses and treatment monitoring.
    • To foster an evolutionary approach to performance goal development.

    Main Methods:

    • Review of the Stockholm 1999 hierarchy and Milan 2014 conference outcomes.
    • Analysis of Jim Westgard's critique of performance goals and
    • total error
    • theory.
    • Comparison of
    • total error
    • theory with generally accepted uncertainty theory.

    Main Results:

    • The Milan conference rearranged performance goals and established task groups.
    • Westgard's critique highlights issues with the Milan conference's approach to
    • total error
    • theory.
    • Uncertainty theory, while accepted, presents mathematical and practical challenges in clinical chemistry.

    Conclusions:

    • A thorough debate on the
    • total error
    • theory is necessary.
    • Improved methods are needed to integrate all uncertainty sources for clinical decisions.
    • Performance goal development should be an evolution, not a revolution.