Ambient conditions. Ambient environmental factors — like pressure, temperature, and humidity — have significant effects on the results of calibration. Instruments should be calibrated in an environment that resembles the one during which they're going to operate.
All measurements have a degree of uncertainty regardless of precision and accuracy. This is caused by two factors, the limitation of the measuring instrument (systematic error) and the skill of the experimenter making the measurements (random error).
There are two types of errors: random and systematic. Random error occurs due to chance. There is always some variability when a measurement is made. Random error may be caused by slight fluctuations in an instrument, the environment, or the way a measurement is read, that do not cause the same error every time.
Accuracy refers to the closeness of a measured value to a standard or known value. For example, if in lab you obtain a weight measurement of 3.2 kg for a given substance, but the actual or known weight is 10 kg, then your measurement is not accurate. In this case, your measurement is not close to the known value.
Because the potential error is greater, the measure is less precise. Thus, as the length of the unit increases, the measure becomes less precise. The number of decimal places in a measurement also affects precision.
Random error mainly affects precision, which is how reproducible the same measurement is under equivalent circumstances. In contrast, systematic error affects the accuracy of a measurement, or how close the observed value is to the true value.
The accuracy of a measurement system has three components: bias, linearity, and stability.
In general, the main disadvantage of accuracy is that it masks the issue of class imbalance. For example if the data contains only 10% of positive instances, a majority baseline classifier which always assigns the negative label would reach 90% accuracy since it would correctly predict 90% instances.
Measurement errors are commonly ascribed to four sources: the respondent, the interviewer, the instrument (i.e., the survey questionnaire), and the mode of data collection. The unique characteristics of business populations and business surveys contribute to the occurrence of specific measurement errors.
There are three types of errors that are classified based on the source they arise from; They are: Gross Errors. Random Errors. Systematic Errors.
There are three major sources of measurement error: gross, systematic, and random.
The biggest threat to the accuracy and reliability of data in applied behavior analysis is human error. Three factors that contribute to threats to measurement accuracy and reliability include poorly designed measurement systems, inadequate observer training, and expectations about what the data should look like.
The variation in the value of the measured quantity, which can be small or large, is called 'uncertainty'. Some limitations in measurements are the result of the measuring instruments.
Accuracy, Sensitivity, Range, Resolution, and Precision. Each of the conversion devices used in a measurement has these qualities. The accuracy is the degree to which the relationship between the output quantity and the input quantity is known at the moment of the measurement.
The ability of an instrument to measure the accurate value is known as accuracy. In other words, it is the the closeness of the measured value to a standard or true value. Accuracy is obtained by taking small readings. The small reading reduces the error of the calculation.
Accuracy and Precision: This characteristic refers to the exactness of the data. It cannot have any erroneous elements and must convey the correct message without being misleading.
Common data entry mistakes are transcription errors & transposition errors.
Those factors that make errors more or less likely are identified (such as poor design, distraction, time pressure, workload, competence, morale, noise levels and communication systems) - Performance Influencing Factors (PIFs) ( PDF )
Data validation ensures the accuracy and reliability of input data by comparing the same with some predetermined standards or known data.
The more data added, the broader the purview to the problem, creating increased accuracy and trust in the results.
In a laboratory situation, high precision with low accuracy often results from a systematic error. Either the measurer makes the same mistake repeatedly, or the measuring tool is somehow flawed. A poorly calibrated balance may give the same mass reading every time, but it will be far from the true mass of the object.