Representing quantities of various attributes relating to a real time system, using numerical values, is known as Measurement.
It can be realized as a comparison between the quantity of unknown magnitude and a predefined standard. Advancement in science and technology is of little significance without the availability of actual measured values to provide practical proofs.
A scientific research is actually based on hypothesis, which is validated only with the help of obtained measured values. The researcher can differentiate between various degrees of the measured attributes and can give a finite value to the occurrences in real time. Measurements are important in reducing the assumption work and provide more objectivity to the findings.
A physical means or device for determining an attribute or variable is known as an instrument. An instrument serves as an aid for humans in determining values of unknown quantities. An instrument can be mechanical, electrical or electronic. A basic instrument consists of a detector, a transfer device and an indicator, recorder or a storage device. Mechanical instruments are the oldest used instruments.
Though reliable for static and stable conditions, they are not appropriate for dynamic and transient conditions.
Also, they are bulky and are a source of noise. Electrical instruments, though use more rapid method of indicating the output, yet have limitations due to the use of mechanical meters.
Electronic instruments have faster responses and are able to detect dynamic changes in different attributes. An example is a CRO, which follows dynamic or transient changes of the order of microseconds. Before learning the main point regarding errors in instrumentation, let us first go through the following discussion. Based upon the degree of variation of the measured quantity with respect to time, an instrument can have static or dynamic characteristics. Some of the important static characteristics are Accuracy, Sensitivity, Reproducibility, Drift, Static error and dead zone.
When ideal conditions are applied to measure any parameter, the average deviations due to various factors tend to be zero. Average of these infinite number of measured values is termed as True Value.This site uses Akismet to reduce spam.
Learn how your comment data is processed. This is most important and starting topic in measurements. It is defined as the average value of an infinite number of measured values when average deviation due to various contributing factor approaches to zero. It is not possible or impossible to determine the true value of quantity by an experiment means.
It is the approximated value of a true value of a quantity. It can be found by taking a number of readings of an experiment taking into different physical parameters and conditions. The static error is defined as the difference between the measured value and the true value of the quantity. Note: The absolute value of error cannot be determined because of the fact that the true value of quantity cannot be determined accurately.
Static Correction may be defined as the difference between the true value and the measured value of the quantity. It is an error that the manufacturer promises that the error in the instrument or item he is selling is no more than the specified limit. A relative or fractional error is defined as the ratio of the error and the specified or nominal magnitude of the quantity.
It is denoted by. This type of error arises due to human mistakes during reading, recording and calculating measurement values from an instrument. Suppose a person reads 12 as 21 by mistake then this type of error is called Gross error. As shown in the above chart, systematic Errors are classified into 3 types as.
Instrumental errors arise mainly due to inherent shortcomings in the instruments, loading effect of instrument or misuse of an instrument. Environmental errors arises due to instrument external conditions like pressure, temperature, vibrations, humidity, magnetic or electric fields, etc.
Observational errors arise due to human inability to read value correctly. One such example is Parallax. Another example is no two person observe the same situation in the same way. There will always some small deviation in the values measured by the two persons.Integrating Measurement and Uncertainty into Science Instruction.
Numbers presented to students in geoscience always have some error associated with them. Anytime data is presented in class, not only in an instrumentation course, it is important they understand the errors associated with that data. Many times these errors are a result of measurement errors.
Even numerical values obtained from models have errors that are, in part, associated with measurement errors, since observation data is used to initialize the model. Measurement errors generally fall into two categories: random or systematic errors. However even if we know about the types of error we still need to know why those errors exist. We can break these into two basic categories: Instrument errors and Operator errors. Random errors are ones that are easier to deal with because they cause the measurements to fluctuate around the true value.
If we are trying to measure some parameter X, greater random errors cause a greater dispersion of values, but the mean of X still represents the true value for that instrument. A systematic error can be more tricky to track down and is often unknown. This error is often called a bias in the measurement. In chemistry a teacher tells the student to read the volume of liquid in a graduated cylinder by looking at the meniscus.
A student may make an error by reading the volume by looking at the liquid level near the edge of the glass. Thus this student will always be off by a certain amount for every reading he makes.
Errors In Measurement - Absolute Error and Relative Error
This is a systematic error. Instruments often have both systematic and random errors. Now that we know the types of measurement errors that can occur, what factors lead to errors when we take measurements? We can separate this category into 2 basic categories: instrument and operator errors.
Human errors are not always blunders however since some mistakes are a result of inexperience in trying to make a particular measurement or trying to investigate a particular problem. When you purchase an instrument if it is of any real value it comes with a long list of specs that gives a user an idea of the possible errors associated with that instrument.
In labs as a faculty you may be using equipment that is not new, so you should help students be aware of the errors associated with the instrument. If the company that made the instrument still exists you can contact them to find out this information as well. Looking at these carefully can help avoid poor measurements and poor usage of the instrument.
Students when they hand in labs can calculate and represent errors associated with their data which is important for every scientist or future scientist. Some basic information that usually comes with an instrument is:. Other instrument errors include calibration errors. All instruments need to be calibrated. Instruments are calibrated according to theory, standards and other instruments that also have errors.The measurement of an amount is based on some international standards which are completely accurate compared with others.
Generally, measurement of any quantity is done by comparing it with derived standards with which they are not completely accurate. Thus, the errors in measurement are not only due to error in methods, but are also due to derivation being not done perfectly well. It is very important for the operator to take proper care of the experiment while performing on industrial instruments so that the error in measurement can be reduced. An error may be defined as the difference between the measured value and the actual value.
For example, if the two operators use the same device or instrument for finding the errors in measurement, it is not necessary that they may get similar results. There may be a difference between both measurements. Sequentially, to understand the concept of errors in measurement, you should know the two terms that define the error. They are true value and the measured value.
The true value is impossible to find out the truth of quantity by experimental means. It may be defined as the average value of an infinite number of measured values. Measured value can be defined as the estimated value of true value that can be found by taking several measured values during an experiment. Generally errors are classified into three types: systematic errors, random errors and blunders. Gross errors are caused by mistake in using instruments or meterscalculating measurement and recording data results.
The best example of these errors is a person or operator reading pressure gage 1. This may be the reason for gross errors in the reported data, and such errors may end up in calculation of the final results, thus deviating results.
Blunders are final source of errors and these errors are caused by faulty recording or due to a wrong value while recording a measurement, or misreading a scale or forgetting a digit while reading a scale. These blunders should stick out like sore thumbs if one person checks the work of another person.
It should not be comprised in the analysis of data. The measurement error is the result of the variation of a measurement of the true value.Designing a research project takes time, skill and knowledge.
With Qualtrics survey softwarewe make the survey creation process easier, but still you may feel overwhelmed with the scope of your research project. Population specification errors occur when the researcher does not understand who they should survey. This can be tricky because there are multiple people who might consume the product, but only one who purchases it, or they may miss a segment looking to purchase in the future.
Example: Packaged goods manufacturers often conduct surveys of housewives, because they are easier to contact, and it is assumed they decide what is to be purchased and also do the actual purchasing.
In this situation there often is population specification error. The husband may purchase a significant share of the packaged goods, and have significant direct and indirect influence over what is bought.
For this reason, excluding husbands from samples may yield results targeted to the wrong audience. How to avoid this: Understand who purchases your product and why they buy it.
Survey sampling and sample frame errors occur when the wrong subpopulation is used to select a sample, or because of variation in the number or representativeness of the sample that responds, but the resulting sample is not representative of the population concern. Unfortunately, some element of sampling error is unavoidable, but sometimes, it can be predicted.
For instance, in the presidential election between Roosevelt and Landon, the sample frame was from car registrations and telephone directories. The researchers failed to realize that the majority of people that owned cars and telephones were Republicans, and wrongly predicted a Republican victory.
Types of Errors in Measurement
Example: Suppose that we collected a random sample of people from the general U. This sample would not be representative of the general adult population and would influence the data. The entertainment preferences of females would hold more weight, preventing accurate extrapolation to the US general adult population. Sampling error is affected by the homogeneity of the population being studied and sampled from and by the size of the sample. How to avoid this: While this cannot be completely avoided, you should have multiple people reviewing your sample to account for an accurate representation of your target population.
You can also increase the size of your sample so you get more survey participants. Selection error is the sampling error for a sample selected by a non-probability method. When respondents choose to self-participate in a study and only those interested respond, you can end up with selection error because there may already be an inherent bias.Systematic, or biased, errors are errors which consistently yield results either higher or lower than the correct measurement.
While conducting measurements in experiments, there are generally two different types of errors: random or chance errors and systematic or biased errors. Every measurement has an inherent uncertainty.
We therefore need to give some indication of the reliability of measurements and the uncertainties of the results calculated from these measurements. To better understand the outcome of experimental data, an estimate of the size of the systematic errors compared to the random errors should be considered. Random errors are due to the precision of the equipment, and systematic errors are due to how well the equipment was used or how well the experiment was controlled.
Low Accuracy, High Precision : This target shows an example of low accuracy points are not close to center target but high precision points are close together.
In this case, there is more systematic error than random error. High Accuracy, Low Precision : This target shows an example of high accuracy points are all close to center target but low precision points are not close together. In this case, there is more random error than systematic error.
Systematic errors are biases in measurement which lead to a situation wherein the mean of many separate measurements differs significantly from the actual value of the measured attribute.
All measurements are prone to systematic errors, often of several different types. Sources of systematic errors may be imperfect calibration of measurement instruments, changes in the environment which interfere with the measurement process, and imperfect methods of observation. A systematic error makes the measured value always smaller or larger than the true value, but not both. An experiment may involve more than one systematic error and these errors may nullify one another, but each alters the true value in one way only.
Accuracy or validity is a measure of the systematic error. If an experiment is accurate or valid, then the systematic error is very small. Accuracy is a measure of how well an experiment measures what it was trying to measure. This is difficult to evaluate unless you have an idea of the expected value e.
Compare your experimental value to the literature value. If it is within the margin of error for the random errors, then it is most likely that the systematic errors are smaller than the random errors.I have already written articles about the basics of units and measurementdifferent conversion chartsdimensions and dimensional analysis etc. In this article, I focus on the error in measurement, different types of errors and the combination of errors which occurs during the measurement of a physical quantity.
While measuring any physical quantity, it is practically impossible to find its true value. The difference between the true value and the measured value of a physical quantity is called the error in its measurement.
In other words, we can say, the result of every measurement by any measuring instrument contains some uncertainty and this uncertainty is called the error. The accuracy of a measurement is the relative exemption from errors. That is, accuracy is the measure of how close the measured value is to the actual value of the quantity.
For every instrument, there is a minimum value that can be measured accurately. This is called the least count of that instrument. It is 0. Precision describes the limit or resolution of the quantity measured. For example, consider an iron rod of length 12 cm. The scale 1 measures it to be Here scale 1 is more accurate but scale 2 is more precise.
Now another scale 3 measures it to be We can say scale 3 is both accurate and precise. Also, learn significant figures and the rules for rounding off the uncertain digits.
The errors that may occur in the measurement of a physical quantity can be classified into six types: constant error, systematic error, random error, absolute error, relative error and percentage error.
Each type of error in measurement are explained below. Constant errors are those which affect the result by the same amount. For eg: If the reading of a thermometer, when placed in melting ice at normal pressure, is 1 0 C, then the instrument has an error by 1 0 C.
Systematic error Systematic errors are due to some known causes according to a definite law and are tend to be in one direction, either positive or negative.
Types of Measurement & Reasons of Measurement Error
We can minimize the systematic errors by selecting better instruments, by improving the experimental techniques or procedures and by removing personal errors as far as possible.
For a given experimental set-up, these systematic errors may be calculated to a certain extent and the necessary corrections may be applied to the observed readings. There are four sources or types of systematic error: Instrumental error, gross error, error due to external causes and the error due to imperfections.
Instrumental errors are errors due to the apparatus or measuring instruments used.