This document discusses measurement errors and uncertainty. It defines measurement as assigning a number and unit to a property using an instrument. Error is the difference between the measured value and true value. There are two main types of error: random error, which varies unpredictably, and systematic error, which remains constant or varies predictably. Sources of error include the measuring instrument and technique used. Uncertainty is the doubt about a measurement and is quantified with an interval and confidence level, such as 20 cm ±1 cm at 95% confidence. Uncertainty is important for tasks like calibration where it must be reported.
This document discusses errors and uncertainty in measurement. It defines error as the difference between an individual measurement and the true value. Errors can be random or systematic. Sources of error include the measuring instrument, the item being measured, the measurement process, and environmental factors. There are two types of uncertainty - type A which is evaluated statistically from repeated measurements, and type B which is evaluated from other sources like specifications or published data. Calculating uncertainty involves identifying sources of uncertainty, estimating individual uncertainties, and combining them to obtain an overall measurement uncertainty.
This document discusses measurement as a tool for research. It outlines four measurement scales - nominal, ordinal, interval, and ratio scales - and describes what can be done with data on each scale. Nominal scales use numbers as labels, ordinal scales indicate order or ranking, interval scales show differences between values but lack a true zero point, and ratio scales have an absolute zero and allow values to be multiplied and divided. The document also discusses the importance of reliability, which is about consistency of measurement, and validity, which is about accuracy and whether a method truly measures what it intends to measure. High reliability does not guarantee high validity, but a highly valid measurement will generally also be reliable.
The document discusses research methodology and related topics. It begins by defining research methodology as how a researcher systematically designs a study to ensure valid and reliable results. It then covers qualitative and quantitative research methods, instrumentation and measurements in research, data collection, analysis and presentation techniques, research flowcharts, and the differences between verification and validation in research. Finally, it addresses differentiating the level of a research study.
This document discusses concepts related to measurement including units, standards, measuring instruments, and errors. It defines key terms like sensitivity, readability, accuracy, precision, uncertainty, static and dynamic characteristics. It describes different types of measuring instruments, methods of measurement, units in the SI system, and factors that influence measurement like sensitivity, resolution, drift, hysteresis, and errors. It also discusses calibration, correction, and interchangeability as they relate to measurement.
Electronics measurement and instrumentation pptImranAhmad225
This document defines key concepts in measurement and instrumentation. It discusses the definition of metrology and engineering metrology. Measurement is defined as the process of numerical evaluation of a dimension or comparison to a standard. Some key methods of measurement discussed are direct, indirect, comparative, coincidence, contact, deflection, and complementary methods. The document also discusses units and standards, characteristics of measuring instruments like sensitivity, readability, range, accuracy, and precision. It defines uncertainty and errors in instruments.
This document discusses various concepts related to errors and accuracy in chemical analysis. It defines different types of errors like gross errors, systematic errors, and random errors. It explains how to classify errors based on their origin and how to minimize different types of errors. The document also covers key statistical concepts like mean, median, standard deviation, normal distribution, precision and accuracy that are important for understanding errors in chemical analysis.
This document discusses measurement errors and uncertainty. It defines measurement as assigning a number and unit to a property using an instrument. Error is the difference between the measured value and true value. There are two main types of error: random error, which varies unpredictably, and systematic error, which remains constant or varies predictably. Sources of error include the measuring instrument and technique used. Uncertainty is the doubt about a measurement and is quantified with an interval and confidence level, such as 20 cm ±1 cm at 95% confidence. Uncertainty is important for tasks like calibration where it must be reported.
This document discusses errors and uncertainty in measurement. It defines error as the difference between an individual measurement and the true value. Errors can be random or systematic. Sources of error include the measuring instrument, the item being measured, the measurement process, and environmental factors. There are two types of uncertainty - type A which is evaluated statistically from repeated measurements, and type B which is evaluated from other sources like specifications or published data. Calculating uncertainty involves identifying sources of uncertainty, estimating individual uncertainties, and combining them to obtain an overall measurement uncertainty.
This document discusses measurement as a tool for research. It outlines four measurement scales - nominal, ordinal, interval, and ratio scales - and describes what can be done with data on each scale. Nominal scales use numbers as labels, ordinal scales indicate order or ranking, interval scales show differences between values but lack a true zero point, and ratio scales have an absolute zero and allow values to be multiplied and divided. The document also discusses the importance of reliability, which is about consistency of measurement, and validity, which is about accuracy and whether a method truly measures what it intends to measure. High reliability does not guarantee high validity, but a highly valid measurement will generally also be reliable.
The document discusses research methodology and related topics. It begins by defining research methodology as how a researcher systematically designs a study to ensure valid and reliable results. It then covers qualitative and quantitative research methods, instrumentation and measurements in research, data collection, analysis and presentation techniques, research flowcharts, and the differences between verification and validation in research. Finally, it addresses differentiating the level of a research study.
This document discusses concepts related to measurement including units, standards, measuring instruments, and errors. It defines key terms like sensitivity, readability, accuracy, precision, uncertainty, static and dynamic characteristics. It describes different types of measuring instruments, methods of measurement, units in the SI system, and factors that influence measurement like sensitivity, resolution, drift, hysteresis, and errors. It also discusses calibration, correction, and interchangeability as they relate to measurement.
Electronics measurement and instrumentation pptImranAhmad225
This document defines key concepts in measurement and instrumentation. It discusses the definition of metrology and engineering metrology. Measurement is defined as the process of numerical evaluation of a dimension or comparison to a standard. Some key methods of measurement discussed are direct, indirect, comparative, coincidence, contact, deflection, and complementary methods. The document also discusses units and standards, characteristics of measuring instruments like sensitivity, readability, range, accuracy, and precision. It defines uncertainty and errors in instruments.
This document discusses various concepts related to errors and accuracy in chemical analysis. It defines different types of errors like gross errors, systematic errors, and random errors. It explains how to classify errors based on their origin and how to minimize different types of errors. The document also covers key statistical concepts like mean, median, standard deviation, normal distribution, precision and accuracy that are important for understanding errors in chemical analysis.
This document discusses various methods for evaluating the reliability of measurement instruments, including internal consistency, test-retest reliability, interrater reliability, split-half methods, and alternate forms methods. It provides details on calculating and interpreting each type of reliability. Factors that can influence reliability are also examined, such as the number of items, characteristics of test takers, heterogeneity of items and groups, and time between test administrations. The document emphasizes that reliability is important for ensuring measurement tools provide consistent results.
Uncertainty analysis involves quantifying errors in measurement systems and experiments. Total experimental error is determined by finding the precision error due to environmental factors and bias error due to equipment. If possible, the error of each experimental component should be determined to identify areas for minimizing error. Errors propagate in data processing based on functional relationships between variables. Proper experimental design and system calibration can help reduce bias and precision errors.
Measurement theory is the formal and logical theory of necessary and sufficient conditions of attributing measures to objects or events. There are different types of measurement scales including nominal, ordinal, interval, and ratio scales, which provide increasing levels of accurate information about the characteristics being measured. The goals of measurement are accuracy and precision, while validity, reliability, precision, and accuracy are terms used to evaluate the quality of a measurement. Measurement errors can be either systematic, occurring consistently in one direction, or random, varying unpredictably.
This document discusses the concepts of reliability and validity in measuring instruments. Reliability refers to the consistency of a measure in producing the same results over time, while validity refers to whether a measure accurately measures what it intends to measure. The document outlines different types of reliability, including test-retest reliability, internal consistency, and equivalence. It also discusses different types of validity such as content validity, criterion-related validity, and construct validity. Maintaining reliability and establishing validity are important for ensuring measuring instruments provide accurate and useful results.
Error is the difference between the measured and true value, while uncertainty quantifies the doubt about a measurement. Uncertainty comes from sources like repeatability, reproducibility, stability, bias, and reference standards. We report uncertainty with measurements using a confidence level to convey the range we believe the true value lies within. Common ways to express uncertainty are ±1 cm at 95% confidence or best estimate ± uncertainty.
Measurement is the process observing and recording the observations that are collected as part of a research effort.
Process of assigning numbers to objects or observations, the level of measurement being a function of the rules under which the numbers are assigned.
“convert the basic materials of the problem to data”
Ag Extn.504 :- RESEARCH METHODS IN BEHAVIOURAL SCIENCE Pradip Limbani
This document discusses measurement, validity, reliability, and levels of measurement. It provides definitions and methods for testing validity and reliability. There are four levels of measurement - nominal, ordinal, interval, and ratio - which determine the appropriate statistical analyses. Validity is tested through content, predictive, concurrent, and construct validity. Reliability refers to consistency and is estimated through test-retest and internal consistency methods. Measurement is important for research as it allows for goal setting and evaluating outcomes.
The document discusses the paradox and challenges of measurement. It notes that obtaining the true value of a parameter is impossible due to measurement error. While measurements are essential, a reliable measurement can only provide an estimate close to the true value, not the true value itself. The true value is often difficult or impossible to directly measure and can only be estimated. The document outlines strategies for classifying, identifying, and accounting for different sources of error to improve the estimation of true values.
In manufacturing operations, production management includes responsibility for product and process design, planning and control issues involving capacity and ...
This document discusses concepts related to measurement including:
1. Measurement is defined as the numerical evaluation of a dimension or comparison to a standard. Measurements are needed to check quality, tolerances, and validate designs.
2. There are direct, indirect, comparative, coincidence, contact, deflection, and complementary methods of measurement. Measurement instruments can be analog, digital, active, passive, automatic, or manual.
3. Characteristics of measuring instruments include sensitivity, readability, accuracy, precision, resolution, threshold, drift, repeatability, and reproducibility. Static characteristics describe instruments for slowly varying quantities while dynamic characteristics describe fast varying quantities.
Statistical Learning and Model Selection (1).pptxrajalakshmi5921
This document discusses statistical learning and model selection. It introduces statistical learning problems, statistical models, the need for statistical modeling, and issues around evaluating models. Key points include: statistical learning involves using data to build a predictive model; a good model balances bias and variance to minimize prediction error; cross-validation is described as the ideal procedure for evaluating models without overfitting to the test data.
Accuracy refers to how close a measurement is to the true value, while precision refers to the reproducibility of measurements. Accuracy is determined by calculating percentage error compared to the accepted value. Precision depends on the number of significant figures in a measurement as determined by the measuring tool. Random and systematic errors can affect accuracy, while random errors affect precision. The uncertainty of a measurement combines its precision and accuracy errors and is reported with the mean value and at a given confidence level, typically 95%. Propagation of error calculations allow determining the total uncertainty when a value depends on multiple measurements.
What is a measurement and what measurement is not
What is uncertainty of measurement?
Error versus uncertainty
Why is uncertainty of measurement important?
Basic statistics on sets of numbers
The general kinds of uncertainty in any measurement
For a free course, visit - www.theapprentiice.com
The document discusses the key characteristics and performance parameters of measuring instruments. It describes:
1) Static characteristics relate to constant or slowly varying inputs over time and include parameters like accuracy, precision, resolution, sensitivity, and linearity.
2) Dynamic characteristics relate to rapidly varying inputs over time and are represented by differential equations.
3) Measuring instruments are evaluated based on both their static and dynamic characteristics, with static characteristics being most important for time-independent signals.
Errors - pharmaceutical analysis -1, bpharm 1st semester, notes, topic errors
full details and answer about error
TN DR MGR UNIVERSITY
by Kumaran.M.pharm, professor
The document discusses various types of errors that can occur in quantitative chemical analysis, including random errors, systematic errors, determinate errors, indeterminate errors, and errors due to faulty instrumentation, impure reagents, or improper methodology. It also describes ways to minimize errors, such as calibrating apparatus, running blanks and controls, using multiple analytical techniques, and performing replicate measurements. Accuracy is defined as how close a measurement is to the true value, while precision refers to the reproducibility of measurements.
The significant figures in a numerical expression are defined as all those whose values are known with certainty with one additional digit whose value is uncertain.
This document discusses metrology and measurement. It defines metrology as the field concerned with measurement, including theoretical and practical problems related to measurement. It establishes metrology as including the establishment, reproduction, and transfer of measurement standards.
The document outlines the principal fields of metrology, including establishing measurement units and standards, measurement methods and accuracy, measuring instruments, observer capabilities, and gauge design. It describes the types of metrology as scientific, industrial, and legal metrology. Scientific metrology deals with maintaining highest level standards. Industrial metrology ensures adequate functioning of instruments in industry. Legal metrology regulates measuring instruments.
The document also discusses measurement units, errors, accuracy, precision, calibration, and factors that affect measurements
This document discusses various methods for evaluating the reliability of measurement instruments, including internal consistency, test-retest reliability, interrater reliability, split-half methods, and alternate forms methods. It provides details on calculating and interpreting each type of reliability. Factors that can influence reliability are also examined, such as the number of items, characteristics of test takers, heterogeneity of items and groups, and time between test administrations. The document emphasizes that reliability is important for ensuring measurement tools provide consistent results.
Uncertainty analysis involves quantifying errors in measurement systems and experiments. Total experimental error is determined by finding the precision error due to environmental factors and bias error due to equipment. If possible, the error of each experimental component should be determined to identify areas for minimizing error. Errors propagate in data processing based on functional relationships between variables. Proper experimental design and system calibration can help reduce bias and precision errors.
Measurement theory is the formal and logical theory of necessary and sufficient conditions of attributing measures to objects or events. There are different types of measurement scales including nominal, ordinal, interval, and ratio scales, which provide increasing levels of accurate information about the characteristics being measured. The goals of measurement are accuracy and precision, while validity, reliability, precision, and accuracy are terms used to evaluate the quality of a measurement. Measurement errors can be either systematic, occurring consistently in one direction, or random, varying unpredictably.
This document discusses the concepts of reliability and validity in measuring instruments. Reliability refers to the consistency of a measure in producing the same results over time, while validity refers to whether a measure accurately measures what it intends to measure. The document outlines different types of reliability, including test-retest reliability, internal consistency, and equivalence. It also discusses different types of validity such as content validity, criterion-related validity, and construct validity. Maintaining reliability and establishing validity are important for ensuring measuring instruments provide accurate and useful results.
Error is the difference between the measured and true value, while uncertainty quantifies the doubt about a measurement. Uncertainty comes from sources like repeatability, reproducibility, stability, bias, and reference standards. We report uncertainty with measurements using a confidence level to convey the range we believe the true value lies within. Common ways to express uncertainty are ±1 cm at 95% confidence or best estimate ± uncertainty.
Measurement is the process observing and recording the observations that are collected as part of a research effort.
Process of assigning numbers to objects or observations, the level of measurement being a function of the rules under which the numbers are assigned.
“convert the basic materials of the problem to data”
Ag Extn.504 :- RESEARCH METHODS IN BEHAVIOURAL SCIENCE Pradip Limbani
This document discusses measurement, validity, reliability, and levels of measurement. It provides definitions and methods for testing validity and reliability. There are four levels of measurement - nominal, ordinal, interval, and ratio - which determine the appropriate statistical analyses. Validity is tested through content, predictive, concurrent, and construct validity. Reliability refers to consistency and is estimated through test-retest and internal consistency methods. Measurement is important for research as it allows for goal setting and evaluating outcomes.
The document discusses the paradox and challenges of measurement. It notes that obtaining the true value of a parameter is impossible due to measurement error. While measurements are essential, a reliable measurement can only provide an estimate close to the true value, not the true value itself. The true value is often difficult or impossible to directly measure and can only be estimated. The document outlines strategies for classifying, identifying, and accounting for different sources of error to improve the estimation of true values.
In manufacturing operations, production management includes responsibility for product and process design, planning and control issues involving capacity and ...
This document discusses concepts related to measurement including:
1. Measurement is defined as the numerical evaluation of a dimension or comparison to a standard. Measurements are needed to check quality, tolerances, and validate designs.
2. There are direct, indirect, comparative, coincidence, contact, deflection, and complementary methods of measurement. Measurement instruments can be analog, digital, active, passive, automatic, or manual.
3. Characteristics of measuring instruments include sensitivity, readability, accuracy, precision, resolution, threshold, drift, repeatability, and reproducibility. Static characteristics describe instruments for slowly varying quantities while dynamic characteristics describe fast varying quantities.
Statistical Learning and Model Selection (1).pptxrajalakshmi5921
This document discusses statistical learning and model selection. It introduces statistical learning problems, statistical models, the need for statistical modeling, and issues around evaluating models. Key points include: statistical learning involves using data to build a predictive model; a good model balances bias and variance to minimize prediction error; cross-validation is described as the ideal procedure for evaluating models without overfitting to the test data.
Accuracy refers to how close a measurement is to the true value, while precision refers to the reproducibility of measurements. Accuracy is determined by calculating percentage error compared to the accepted value. Precision depends on the number of significant figures in a measurement as determined by the measuring tool. Random and systematic errors can affect accuracy, while random errors affect precision. The uncertainty of a measurement combines its precision and accuracy errors and is reported with the mean value and at a given confidence level, typically 95%. Propagation of error calculations allow determining the total uncertainty when a value depends on multiple measurements.
What is a measurement and what measurement is not
What is uncertainty of measurement?
Error versus uncertainty
Why is uncertainty of measurement important?
Basic statistics on sets of numbers
The general kinds of uncertainty in any measurement
For a free course, visit - www.theapprentiice.com
The document discusses the key characteristics and performance parameters of measuring instruments. It describes:
1) Static characteristics relate to constant or slowly varying inputs over time and include parameters like accuracy, precision, resolution, sensitivity, and linearity.
2) Dynamic characteristics relate to rapidly varying inputs over time and are represented by differential equations.
3) Measuring instruments are evaluated based on both their static and dynamic characteristics, with static characteristics being most important for time-independent signals.
Errors - pharmaceutical analysis -1, bpharm 1st semester, notes, topic errors
full details and answer about error
TN DR MGR UNIVERSITY
by Kumaran.M.pharm, professor
The document discusses various types of errors that can occur in quantitative chemical analysis, including random errors, systematic errors, determinate errors, indeterminate errors, and errors due to faulty instrumentation, impure reagents, or improper methodology. It also describes ways to minimize errors, such as calibrating apparatus, running blanks and controls, using multiple analytical techniques, and performing replicate measurements. Accuracy is defined as how close a measurement is to the true value, while precision refers to the reproducibility of measurements.
The significant figures in a numerical expression are defined as all those whose values are known with certainty with one additional digit whose value is uncertain.
This document discusses metrology and measurement. It defines metrology as the field concerned with measurement, including theoretical and practical problems related to measurement. It establishes metrology as including the establishment, reproduction, and transfer of measurement standards.
The document outlines the principal fields of metrology, including establishing measurement units and standards, measurement methods and accuracy, measuring instruments, observer capabilities, and gauge design. It describes the types of metrology as scientific, industrial, and legal metrology. Scientific metrology deals with maintaining highest level standards. Industrial metrology ensures adequate functioning of instruments in industry. Legal metrology regulates measuring instruments.
The document also discusses measurement units, errors, accuracy, precision, calibration, and factors that affect measurements
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4. Evaluating measurements
• Accuracy
– measure of how close a measurement comes to
the actual or true value of whatever is
measured.
• Precision
– measure of how close a series of
measurements are to one another.
7. Evaluating measurements
• Often, we are experimentally determining a
value in the lab that is already known.
– When we do this, we must calculate error in
order to see how accurate and precise our
results are
• In lab reports, you will be required to
determine your error and percent error.
8. Evaluating measurements
• To determine Error:
– The accepted value is the correct value based
on reliable references.
– The experimental value is the value measured
in the lab.
– The difference between the experimental value
and the accepted value is called the error.
–.
9. Evaluating measurements
• To determine Percent Error:
– The percent error is the absolute value of the
error divided by the accepted value, multiplied
by 100%.
– .
10. Practice: error and percent error
Question 1
• The accepted value for the boiling point of
water is 100.0°C. In the lab, you
experimentally determined it to be 98.7°C.
What is the error and percent error?
11. Practice: error and percent error
Answer 1
• Given
– Accepted value = 100.0°C
– Experimental value = 98.7°C
• Work
– .
– Error = 98.7°C - 100.0°C = -1.3°C
– .