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Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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Accuracy, limits, and approximation01:28

Accuracy, limits, and approximation

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Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
Accuracy is defined as the closeness of the measured value to the true or actual value. In engineering mechanics, repeated measurements are taken during theoretical or experimental analyses to ensure that the result is precise and accurate.
The accuracy of any solution is based on the...
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Uncertainty in Measurement: Reading Instruments02:46

Uncertainty in Measurement: Reading Instruments

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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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Statistical Analysis: Overview01:11

Statistical Analysis: Overview

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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
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Accuracy and Precision01:52

Accuracy and Precision

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.  Highly accurate...
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Accuracy and Errors in Hypothesis Testing01:13

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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
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Related Experiment Video

Updated: Aug 5, 2025

Accuracy in Dental Medicine, A New Way to Measure Trueness and Precision
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Jensen-Inaccuracy Information Measure.

Omid Kharazmi1, Faezeh Shirazinia1, Francesco Buono2

  • 1Department of Statistics, Faculty of Mathematical Sciences, Vali-e-Asr University of Rafsanjan, Rafsanjan 7718897111, Iran.

Entropy (Basel, Switzerland)
|March 29, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces the Jensen-inaccuracy measure for information theory. It explores its properties, connections to other measures, and applications in optimization and image processing.

Keywords:
Jensen–inaccuracy measureRényi entropyaverage entropyinaccuracy measureoptimal information

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Area of Science:

  • Information Theory
  • Mathematical Analysis

Background:

  • Existing information measures have limitations in certain applications.
  • The need for novel measures to capture specific data properties is recognized.

Purpose of the Study:

  • Introduce and analyze the novel Jensen-inaccuracy measure.
  • Investigate its relationship with established information measures.
  • Demonstrate its utility in optimization and real-world data analysis.

Main Methods:

  • Theoretical examination of the Jensen-inaccuracy measure's properties.
  • Comparative analysis with existing information measures.
  • Application in three distinct optimization problems.
  • Empirical validation using image processing examples.

Main Results:

  • The Jensen-inaccuracy measure is formally defined and its characteristics are detailed.
  • Connections between Jensen-inaccuracy, inaccuracy, and other information measures are established.
  • Optimal information properties of arithmetic mixture distributions are shown using the inaccuracy measure.
  • Numerical results demonstrate the measure's performance in image processing tasks.

Conclusions:

  • The Jensen-inaccuracy measure offers a valuable addition to the field of information theory.
  • Its properties and applications provide new insights for data analysis and optimization.
  • The measure shows practical relevance in fields like image processing.