Technical errors can be divided into two categories: accidental errors and systematic errors. As the name suggests, random errors occur at regular intervals, with no apparent motive. Systematic errors occur when there is a problem with the instrument. For example, a scale could be poorly calibrated and read 0.5 g with nothing on it. All measures would therefore be overestimated by 0.5 g. If you don`t take this into account to your extent, your measurement contains a few errors. Note that, in this context, the terms of truth and precision defined in ISO 5725-1 are not applicable. One reason is that there is not a single “real value” of a quantity, but two possible true values for each case, whereas in all cases, accuracy is an average and therefore takes into account both values. In this context, however, the term “precision” is described as another metric that comes from the field of the call for information (see below). With the publication of the ISO 5725 series of standards in 1994, the meaning of these terms has changed, which is also reflected in the 2008 edition of the BIPM International Vocabulary of Metrology (VIM), items 2.13 and 2.14.
[2] If you perform a series of replication tests (i.e. identical in all respects), you will probably get scattered results. The actual value of the volume of water in the bottle is 50.00 ± 0.06 ml. This means that the actual value of the volume of water in the stolen bottle could reach 50.00 – 0.06 – 49.94 ml or up to 50.00 – 0.06 – 50.06 ml. For the student`s measurements to be considered accurate, the value obtained must be between 49.94 ml and 50.06 ml. The determined value is the average measure of the volume the student records with 49.89 ml. The value of the student is less than the lowest real value for a given measure. For example, as we know, the real value of the mass of an iron cube is 7.90 g.
They weigh the same cube of iron and find that it has a mass of 7.90 g. The actual value and value are the same, so we can say that we have accurately determined the mass of the iron cube. In numerical analysis, accuracy is also the proximity of a calculation to the actual value; While accuracy is the resolution of representation, it is usually defined by the number of decimals or binaries. In accordance with ISO 5725-1[1], the general term “precision” is used to describe the proximity of a measurement to the actual value. When the term is applied to series of measurements of the same measurement, it is a component of random errors and a component of systematic errors. In this case, the veracity is the proximity of the average of a set of measurement results to the actual value and the accuracy is the agreement between a number of results. In logic simulation, a common error in evaluating specific models is to compare a logic simulation model with a transistor simulation model. This is a comparison of differences in accuracy, not precision. Accuracy is measured in terms of detail and accuracy in relation to reality.
[11] [12] The accuracy is how close a measurement is to the correct value for that measurement. The accuracy of a measurement system refers to the proximity of the concordance between repeated measurements (repeated under the same conditions). Measurements can be both accurate and precise, accurate, but not precise, accurate, but not accurate, but not accurate or not. In simpler terms, it can be said that the set is accurate for a series of data points from repeated measurements of the same amount if their average is close to the actual value of the measured quantity, while the set can be described as accurate when the values are close to each other. In the first, more common definition of “precision” above, the two concepts are independent of each other, so that a data set can be characterized as accurate or accurate or both or both or both.