Errors: types, determination and elimination
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Measurement Uncertainty
This document discusses measurement uncertainty. It defines measurement uncertainty as a parameter included with any measurement result that accounts for possible errors. It describes sources of uncertainty like sampling, storage conditions, and personal effects. The document outlines methods of calculating uncertainty using the standard deviation, and explains why assessing uncertainty is important for interpreting results and ensuring measurement quality. Measurement uncertainty is a key component of any measurement result.
Data analysis
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.
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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.
Errors - pharmaceutical analysis -1
Errors - pharmaceutical analysis -1, bpharm 1st semester, notes, topic errors
full details and answer about error
TN DR MGR UNIVERSITY
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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.
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Error is defined as the numerical difference between a measured value and the absolute or true value of an analytical determination.
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Here are the steps to calculate sensitivity, specificity, positive predictive value, negative predictive value, and efficiency for the given diagnostic test:
* True Positives (TP) = Number of HIV+ samples correctly identified as positive by the test = 120 - 15 = 105
* True Negatives (TN) = Number of HIV- samples correctly identified as negative by the test = 300 - (120 + 4) = 176
* False Positives (FP) = Number of HIV- samples incorrectly identified as positive by the test = 4
* False Negatives (FN) = Number of HIV+ samples incorrectly identified as negative by the test = 15
Sensitivity = TP / (TP + FN) = 105 / (105
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This document discusses measurement uncertainty. It defines measurement uncertainty as a parameter included with any measurement result that accounts for possible errors. It describes sources of uncertainty like sampling, storage conditions, and personal effects. The document outlines methods of calculating uncertainty using the standard deviation, and explains why assessing uncertainty is important for interpreting results and ensuring measurement quality. Measurement uncertainty is a key component of any measurement result.
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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.
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Errors - pharmaceutical analysis -1
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full details and answer about error
TN DR MGR UNIVERSITY
by Kumaran.M.pharm, professor
Errors
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.
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Error is defined as the numerical difference between a measured value and the absolute or true value of an analytical determination.
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Here are the steps to calculate sensitivity, specificity, positive predictive value, negative predictive value, and efficiency for the given diagnostic test:
* True Positives (TP) = Number of HIV+ samples correctly identified as positive by the test = 120 - 15 = 105
* True Negatives (TN) = Number of HIV- samples correctly identified as negative by the test = 300 - (120 + 4) = 176
* False Positives (FP) = Number of HIV- samples incorrectly identified as positive by the test = 4
* False Negatives (FN) = Number of HIV+ samples incorrectly identified as negative by the test = 15
Sensitivity = TP / (TP + FN) = 105 / (105
ERRORS
This document discusses errors in measurement and analysis. It defines absolute and relative errors as the difference between experimental and true values. Errors are classified as determinate (systemic) or indeterminate (random). Determinate errors include personal, instrumental, method, additive and proportional errors. Indeterminate errors cannot be avoided and come from unknown causes. Accuracy refers to how close a measurement is to the true value, while precision describes the reproducibility of measurements. Significant figures convey the precision or accuracy of numerical values. The document provides examples and rules for determining significant figures.
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• Measurement uncertainty vs. measurement error
• Bottom- up approach for estimating uncertainty
• Top- down approach for estimating uncertainty
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Data Interpretation Issues
Learning Objectives
• Distinguish between random and
systematic errors
• State and describe sources of bias
• Identify techniques to reduce bias at the
design and analysis phases of a study
• Define what is meant by the term
confounding and provide three examples
• Describe methods to control confounding
Validity of Study Designs
• The degree to which the inference drawn
from a study, is warranted when account it
taken of the study, methods, the
representativeness of the study sample,
and the nature of the population from
which it is drawn.
Validity of Study Designs
• Two components of validity:
– Internal validity
– External validity
Internal Validity
• A study is said to have internal validity
when there have been proper selection of
study groups and a lack of error in
measurement.
• Concerned with the appropriate
measurement of exposure, outcome, and
association between exposure and
disease.
External Validity
• External validity implies the ability to
generalize beyond a set of observations to
some universal statement.
• A study is externally valid, or
generalizable, if it allows unbiased
inferences regarding some other target
population beyond the subjects in the
study.
Sources of Error in
Epidemiologic Research
• Random errors
• Systematic errors (bias)
Random Errors
• Reflect fluctuations around a true value of
a parameter because of sampling
variability.
Factors That Contribute to
Random Error
• Poor precision
• Sampling error
• Variability in measurement
Poor Precision
• Occurs when the factor being measured is
not measured sharply.
• Analogous to aiming a rifle at a target that
is not in focus.
• Precision can be increased by increasing
sample size or the number of
measurements.
• Example: Bogalusa Heart Study
Sampling Error
• Arises when obtained sample values
(statistics) differ from the values
(parameters) of the parent population.
• Although there is no way to prevent a
non-representative sample from
occurring, increasing the sample size
can reduce the likelihood of its
happening.
Variability in Measurement
• The lack of agreement in results from
time to time reflects random error
inherent in the type of measurement
procedure employed.
Bias (Systematic Errors)
• “Deviation of results or inferences
from the truth, or processes leading to
such deviation. Any trend in the
collection, analysis, interpretation,
publication, or review of data that can
lead to conclusions that are
systematically different from the
truth.”
Factors That Contribute to
Systematic Errors
• Selection bias
• Information bias
• Confounding
Selection Bias
• Refers to distortions that result from procedures
used to select subjects and from factors that
influence participation in the study.
• Arises when the relation between exposure and
disease is different for th ...
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Errors in research can be defined as the difference between observed or calculated values and the true values. There are two types of errors: sampling errors, which result from chance selection in sampling, and non-sampling errors from other sources. Non-sampling errors include errors from incorrectly specifying the target population, using non-random selection methods, having an incomplete sampling frame, non-response, surrogate data, measurement issues, experimental design flaws, errors in interviews, materials, observations, concepts, and communication. Researchers can reduce errors through careful research design and by estimating and measuring the errors that cannot be eliminated.
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This document discusses different types of errors that can occur in analytical chemistry measurements and methods. It describes determinate errors, which include instrumental errors from faulty tools, methodic errors from defective experimental methods, operational errors from improper technique, and personal errors from the analyst. It also discusses indeterminate errors, which are random errors that cannot be attributed to a known cause. The document explains how errors can propagate in calculations and discusses accuracy and precision in measurements.
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Errors.pptx
Errors refer to the differences between measured and true values in measurements and experiments. It is impossible to perform analyses that are completely free of errors. Errors are caused by faulty instruments, imprecise measurements, and random variations. Methods to reduce errors include frequent calibration of instruments, analysis of known samples, and repeating measurements. Precision refers to the reproducibility of measurements and can be estimated through repeated measurements of replicate samples. Accuracy is the closeness of a measurement to the true value and is more difficult to determine than precision. There are two main types of errors: determinate errors caused by mistakes that can be avoided, and accidental errors that are difficult to control. Methods to minimize errors include calibration, using blanks, comparative analysis techniques, and repeated
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#Scope
#Objective
#Sources & Types Of Errors
#Impurities in Pharmaceuticals
#Limit Test For
*Chloride
*Sulphate
*Iron
*Heavy Metal
*Arsenic
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Main points covered:
• Measurement uncertainty vs. measurement error
• Bottom- up approach for estimating uncertainty
• Top- down approach for estimating uncertainty
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This webinar was presented by Bahar Hosseinzadeh, PECB Certified Trainer and Sales Manager, ISO/IEC 17025 Consultancy Project Manager at PQP Ltd.
Link of the recorded session published on YouTube: https://youtu.be/kR47GnhNjPw
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Learning Objectives
• Distinguish between random and
systematic errors
• State and describe sources of bias
• Identify techniques to reduce bias at the
design and analysis phases of a study
• Define what is meant by the term
confounding and provide three examples
• Describe methods to control confounding
Validity of Study Designs
• The degree to which the inference drawn
from a study, is warranted when account it
taken of the study, methods, the
representativeness of the study sample,
and the nature of the population from
which it is drawn.
Validity of Study Designs
• Two components of validity:
– Internal validity
– External validity
Internal Validity
• A study is said to have internal validity
when there have been proper selection of
study groups and a lack of error in
measurement.
• Concerned with the appropriate
measurement of exposure, outcome, and
association between exposure and
disease.
External Validity
• External validity implies the ability to
generalize beyond a set of observations to
some universal statement.
• A study is externally valid, or
generalizable, if it allows unbiased
inferences regarding some other target
population beyond the subjects in the
study.
Sources of Error in
Epidemiologic Research
• Random errors
• Systematic errors (bias)
Random Errors
• Reflect fluctuations around a true value of
a parameter because of sampling
variability.
Factors That Contribute to
Random Error
• Poor precision
• Sampling error
• Variability in measurement
Poor Precision
• Occurs when the factor being measured is
not measured sharply.
• Analogous to aiming a rifle at a target that
is not in focus.
• Precision can be increased by increasing
sample size or the number of
measurements.
• Example: Bogalusa Heart Study
Sampling Error
• Arises when obtained sample values
(statistics) differ from the values
(parameters) of the parent population.
• Although there is no way to prevent a
non-representative sample from
occurring, increasing the sample size
can reduce the likelihood of its
happening.
Variability in Measurement
• The lack of agreement in results from
time to time reflects random error
inherent in the type of measurement
procedure employed.
Bias (Systematic Errors)
• “Deviation of results or inferences
from the truth, or processes leading to
such deviation. Any trend in the
collection, analysis, interpretation,
publication, or review of data that can
lead to conclusions that are
systematically different from the
truth.”
Factors That Contribute to
Systematic Errors
• Selection bias
• Information bias
• Confounding
Selection Bias
• Refers to distortions that result from procedures
used to select subjects and from factors that
influence participation in the study.
• Arises when the relation between exposure and
disease is different for th ...
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Errors in research can be defined as the difference between observed or calculated values and the true values. There are two types of errors: sampling errors, which result from chance selection in sampling, and non-sampling errors from other sources. Non-sampling errors include errors from incorrectly specifying the target population, using non-random selection methods, having an incomplete sampling frame, non-response, surrogate data, measurement issues, experimental design flaws, errors in interviews, materials, observations, concepts, and communication. Researchers can reduce errors through careful research design and by estimating and measuring the errors that cannot be eliminated.
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This document discusses various techniques for analyzing quantitative and qualitative data in research. It outlines different statistical procedures that can be used depending on the type of data, such as descriptive statistics for descriptive research data, correlations for examining relationships between variables, and t-tests or analysis of variance for experimental data involving comparisons between groups. Both parametric and non-parametric statistical methods are covered. The document also addresses qualitative data analysis and multivariate analysis techniques like multiple regression, discriminant analysis, and factor analysis.
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This document discusses different types of errors that can occur in analytical chemistry measurements and methods. It describes determinate errors, which include instrumental errors from faulty tools, methodic errors from defective experimental methods, operational errors from improper technique, and personal errors from the analyst. It also discusses indeterminate errors, which are random errors that cannot be attributed to a known cause. The document explains how errors can propagate in calculations and discusses accuracy and precision in measurements.
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Researchers must carefully screen data before conducting statistical analysis to address issues that could impact results. This involves evaluating accuracy, assessing effects of missing or outlier data, and determining if the data fits assumptions. Specifically, researchers should check for errors, missing patterns, extreme outliers, normality, linear relationships, and equal variability across values. Addressing these quality issues through data screening allows researchers to have confidence in their analysis and conclusions drawn from the data.
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#Objective
#Sources & Types Of Errors
#Impurities in Pharmaceuticals
#Limit Test For
*Chloride
*Sulphate
*Iron
*Heavy Metal
*Arsenic
PPT on Sample Size, Importance of Sample Size,
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Errors: types, determination and elimination
- 1. • Types of errors • Random • Systematic errors • Methods of detection and elimination of systematic errors • Student‘s t-test Topics to be covered in this slide
- 2. ERRORS • It is defined as the difference between the observed or measured value and the true and accepted value in an analysis. • They usually affect the accuracy and precision.
- 3. Types of errors Random or indeterminate Systematic or determinate
- 4. RANDOM ERRORS • Errors whose magnitude cannot be determined and their effects cannot be eliminated. • These errors cause small random variations in the measured value when the measurement is made a number of times. • Indeterminate errors will affect the precision. • These occur accidentally hence they are called
- 5. SYSTEMATIC ERRORS • Errors whose magnitude can be determined and their effects can be eliminated are called systematic errors. • These errors cause the value to differ from the accepted and true value. • These error affect the accuracy of the results. • Greater the error lesser will be the accuracy
- 7. SYSTEMATIC ERRORS SYSTEMATIC ERRORS INSTRUMENTAL ERROR METHODIC ERROR PERSONAL OR OPERATIVE ERRORS
- 8. INSTRUMENTAL ERRORS • These errors are caused by the usage of defective instruments and instabilities in the power supply. • These are detected and eliminated by calibration • Periodic calibration of the instrument is
- 9. METHODIC ERROR BY USING STANDARD SAMPLES BY USING INDEPEND ENT ANALYSIS BY RUNNING A BLANK DETERMIN ATION VARIATIO N IN SAMPLE SIZE
- 10. BY USING STANDARD SAMPLE • The best way of determining this type of errors is by doing an analysis with the standard reference material. • The standard reference material is to be synthesized or bought from industrial sources.
- 11. BY USING INDEPENDENT ANALYSIS • If standard samples are not available, a second independent or reliable method can be performed parallel to analytical method being evaluated
- 12. VARIATION IN SAMPLE SIZE • As the size of sample increases , the effect of systematic error decreases. • Hence this type of error can be detected and eliminated by running the experiment by changing the sample size.
- 13. PERSONAL OR OPERATIVE ERRORS • These errors are caused by carelessness , inattention physical inability and a wrong way of using instruments. • These can be reduced by care and self discipline and good knowledge in handling
- 14. COMPARRISON OF RESULTS • The value obtained from a set of results are compared with either the true value or standard value.
- 16. STUDENT’S -T - TEST • Comparison of experimental mean with a true value. • t=Іx-μІ√n/s Where , x is the mean μ is the true value n is the number of results S is the standard
- 17. THE DESIRED CONFIDENCE LEVEL NUMBER OF DEGREES OF FREEDOM
- 18. • The t value is related to the t table which provides the value of t for a few degrees of freedom. • If the calculated value of t is greater than that of the values in the t table then the result is significant. • If the experimental value is less than that of the given t table