Aspects of validation

The most tested attributes in validation tasks may include, but are not limited to

Accuracy

In the fields of science, engineering, industry and statistics, the accuracy of a measurement system is the degree of closeness of measurements of a quantity to its actual (true) value. The precision of a measurement system, also called reproducibility or repeatability, is the degree to which repeated measurements under unchanged conditions show the same results. Although the two words can be synonymous in colloquial use, they are deliberately contrasted in the context of the scientific method.

Accuracy indicates proximity of measurement results to the true value, precision to the repeatability or reproducibility of the measurement

A measurement system can be accurate but not precise, precise but not accurate, neither, or both. For example, if an experiment contains a systematic error, then increasing the sample size generally increases precision but does not improve accuracy. Eliminating the systematic error improves accuracy but does not change precision.

A measurement system is called valid if it is both accurate and precise. Related terms are bias (non-random or directed effects caused by a factor or factors unrelated by the independent variable) and error (random variability), respectively.

The terminology is also applied to indirect measurements, that is, values obtained by a computational procedure from observed data.

In addition to accuracy and precision, measurements may have also a measurement resolution, which is the smallest change in the underlying physical quantity that produces a response in the measurement.

Repeatability or test-retest reliability is the variation in measurements taken by a single person or instrument on the same item and under the same conditions. A measurement may be said to be repeatable when this variation is smaller than some agreed limit. According to the Guidelines for Evaluating and Expressing the Uncertainty of NIST Measurement Results, repeatability conditions include:

the same measurement procedure

the same observer

the same measuring instrument, used under the same conditions

the same location

repetition over a short period of time.

Reproducibility is one of the main principles of the scientific method, and refers to the ability of a test or experiment to be accurately reproduced, or replicated, by someone else working independently.

The results of an experiment performed by a particular researcher or group of researchers are generally evaluated by other independent researchers who repeat the same experiment themselves, based on the original experimental description (see independent review). Then they see if their experiment gives similar results to those reported by the original group. The result values are said to be commensurate if they are obtained (in distinct experimental trials) according to the same reproducible experimental description and procedure.

Reproducibility is different from repeatability, which measures the success rate in successive experiments, possibly conducted by the same experimenters. Reproducibility relates to the agreement of test results with different operators, test apparatus, and laboratory locations. It is often reported as a standard deviation.

While repeatability of scientific experiments is desirable, it is not considered necessary to establish the scientific validity of a theory. For example, the cloning of animals is difficult to repeat, but has been reproduced by various teams working independently, and is a well established research domain. One failed cloning does not mean that the theory is wrong or unscientific.

Limit of detection; Limit of quantification

In analytical chemistry, the detection limit, lower limit of detection, or LOD (limit of detection), is the lowest quantity of a substance that can be distinguished from the absence of that substance (a blank value) within a stated confidence limit (generally 1%). The detection limit is estimated from the mean of the blank, the standard deviation of the blank and some confidence factor. Another consideration that affects the detection limit is the accuracy of the model used to predict concentration from the raw analytical signal.

There are a number of different " detection limits" that are commonly used. These include the instrument detection limit (IDL), the method detection limit (MDL), the practical quantification limit (PQL), and the limit of quantification (LOQ). Even when the same terminology is used, there can be differences in the LOD according to nuances of what definition is used and what type of noise contributes to the measurement and calibration.

Curve fitting is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points, possibly subject to constraints. Curve fitting can involve either interpolation, where an exact fit to the data is required, or smoothing, in which a " smooth" function is constructed that approximately fits the data. A related topic is regression analysis, which focuses more on questions of statistical inference such as how much uncertainty is present in a curve that is fit to data observed with random errors. Fitted curves can be used as an aid for data visualization, to infer values of a function where no data are available, and to summarize the relationships among two or more variables. Extrapolation refers to the use of a fitted curve beyond the range of the observed data, and is subject to a greater degree of uncertainty since it may reflect the method used to construct the curve as much as it reflects the observed data.

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