Technology & Engineering
Sources of Error in Experiments
Sources of error in experiments refer to the factors that can affect the accuracy and precision of the results obtained. These errors can be classified as systematic or random errors, and can arise from various sources such as equipment limitations, human error, environmental factors, and experimental design. It is important to identify and minimize these errors to ensure reliable and valid experimental results.
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eBook - ePubMetrology and Instrumentation
Practical Applications for Engineering and Manufacturing
- Samir Mekid(Author)
- 2021(Publication Date)
- Wiley-ASME Press Series(Publisher)
4 Errors and their Sources“If you wanna master the error, look for its source!”INTRODUCTION
Metrology's measurement error is an important subset. Manufacturing of parts or artifacts will always result in dimensional lengths that are within or outside of the specified tolerance, so it is necessary to measure the current length and determine its true measurement. The measurement process, on the other hand, is always subject to some degree of uncertainty. Measurements are not exact, and they should be reported as a range of values rather than a single value. Several examples can be found in our daily lives. If a manufactured part is given to two technicians to be dimensioned, the results will be slightly different. Furthermore, if you measure the speed of your car using the in‐car speed meter, the result may differ slightly from the police speed gun reading outside. In this case, we changed the instrument, introducing a new type of error.This chapter will define error and its various possible sources, how error propagates in measurement, errors associated with motion, error classification, and error elimination. An estimation of error or uncertainty analysis is a tool for determining the performance capability of machine tools and highlighting potential areas for performance and cost improvement. Machine designers are keenly interested in the implementation of this process. Aspects of measurement characterization used in this book will be defined later.4.1 Definition of the Error and Their Types
The error is defined as the amount by which a measured value deviates from its true value. The error is inextricably linked to the accuracy. It is attributed to three major causes: human measurement errors, the measuring method, and the instrument. The errors are divided into three categories: gross, random, and systematic.- Gross errors are a consequence of human mistakes: wrong measurement readings and recording incorrect data. Readings should be taken with care, and several readings of the same quantity should be taken and recorded.
eBook - PDFDealing with Data
The Commonwealth and International Library: Physics Division
- Arthur J. Lyon, W. Ashhurst(Authors)
- 2013(Publication Date)
- Pergamon(Publisher)
CHAPTER 1 EXPERIMENTAL ERRORS 1. ERROR AND UNCERTAINTY IN MEASUREMENTS The data, i.e. the numerical results of measurements, with which we have to deal in the physical sciences are invariably subject in some degree to errors and uncertainty. By errors, in this context, we do not mean mistakes. Examples of mistakes in experimental work are of mis-readings of a scale, faulty ad-justments, careless handling of equipment, failure to take essen-tial precautions, arithmetic slips in calculation, and so forth. But all of these are faults which can be eliminated or at least reduced to a minimum by care, practice, and experience. A mistake is a departure from the correct, prescribed, or intended procedure for the experiment; an error, on the other hand, in the context of measurements, means a departure of the measured value from the true value which occurs even when the prescribed procedure has been followed correctly. 1 Such departures, experimental errors as they are called, can arise from many sources, including imperfections of the equip-ment and effects of the surrounding physical conditions. There are, in fact, many kinds of experimental error, and the follow-ing sections (§§ 2-10) will attempt to describe the most impor-tant kinds and to illustrate them in relation to simple measure-ments such as all students in the physical sciences will common-ly meet. Often experimental errors can be corrected or reduced in magnitude by improvements of technique, but liability to such t For a discussion of some ambiguities in this definition see § 21.1. 2 DEALING WITH DATA errors can never be eliminated entirely. It is therefore impor-tant to be able to assess the magnitude of the experimental error liable to be present in the result of a measurement. The methods of doing so will be dealt with in later chapters, but some brief preliminary comments may be useful. There are, in fact, two principal modes of assessment in common use.
- Richard S. Figliola, Donald E. Beasley(Authors)
- 2015(Publication Date)
- Wiley(Publisher)
But it does not address all of the possible errors that influence a measured result. Here we provide a helpful checklist of common errors. It is not necessary to classify error sources as we do here, but it is a good bookkeeping practice. Consider the measurement process as consisting of three distinct stages: calibration, data acquisition, and data reduction. Errors that enter during each of these steps can be grouped under their respective error source heading: (1) calibration errors, (2) data-acquisition errors, and (3) data- reduction errors. Within each of these three error source groups, list the types of errors encountered. Such errors are the elemental errors of the measurement. Later, we will assign uncertainty values to each error. Do not become preoccupied with these groupings. Use them as a guide. If you place an error in an ‘‘incorrect’’ group, it is okay. The final uncertainty is not changed! Calibration Errors Calibration in itself does not eliminate system errors but it can help to quantify the uncertainty in the particular pieces of equipment used. Calibration errors include those elemental errors that enter the measuring system during its calibration. Calibration errors tend to enter through three sources: (1) the standard or reference value used in the calibration, (2) the instrument or system under calibration, and (3) the calibration process. For example, the laboratory standard used for calibration contains some inherent uncertainty, and this is passed along with the input value on which the calibration is based. Measuring system errors, such as linearity, repeatability, hysteresis, and so forth, contribute uncertainty. Depending on how the calibration is done, there can be a difference between the value supplied by the standard and the value actually sensed by the measuring system. These effects are built into the calibration data. In Table 5.1, we list the common elemental errors contributing to this error source group.
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