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Related Concept Videos

Uncertainty: Overview00:59

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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.
Uncertainty in Measurement: Significant Figures03:34

Uncertainty in Measurement: Significant Figures

All the digits in a measurement, including the uncertain last digit, are called significant figures or significant digits. Note that zero may be a measured value; for example, if a scale that shows weight to the nearest pound reads “140,” then the 1 (hundreds), 4 (tens), and 0 (ones) are all significant (measured) values.
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

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...
Uncertainty in Measurement: Reading Instruments02:46

Uncertainty in Measurement: Reading Instruments

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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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
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Three ways to quantify uncertainty in individually applied "minimally important change" values.

Henrica C W de Vet1, Berend Terluin, Dirk L Knol

  • 1EMGO Institute for Health and Care Research, VU University Medical Center, 1081 BT Amsterdam, The Netherlands. hcw.devet@vumc.nl

Journal of Clinical Epidemiology
|June 23, 2009
PubMed
Summary

Interpreting minimally important change (MIC) values for individual patients requires understanding uncertainty. This study quantifies MIC uncertainty using confidence intervals, sensitivity, specificity, and smallest detectable change.

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Area of Science:

  • Health Measurement
  • Clinical Outcomes Assessment
  • Psychometrics

Background:

  • Minimally important change (MIC) scores are crucial for interpreting patient-reported outcomes.
  • Understanding MIC at the individual level is essential for clinical decision-making.
  • Quantifying uncertainty in MIC estimates is vital for accurate interpretation.

Purpose of the Study:

  • To explain how to interpret minimally important change (MIC) values for individual patients.
  • To demonstrate methods for quantifying the uncertainty associated with MIC values.
  • To highlight the importance of considering measurement error and statistical uncertainty.

Main Methods:

  • Determined the MIC for a hypothetical questionnaire 'Q' in a sample of 500 patients.
  • Employed the receiver operating characteristic (ROC) method to establish the MIC.
  • Utilized three methods to quantify uncertainty: 95% confidence intervals, sensitivity/specificity, and smallest detectable change (SDC).

Main Results:

  • The MIC for questionnaire Q was determined to be 10.5.
  • The 95% confidence interval for the MIC was 5.6-14.2, indicating estimation uncertainty.
  • Sensitivity was 74% and specificity was 91%, quantifying confidence in the MIC for individual patients.
  • The smallest detectable change (SDC) was calculated as 16.0, providing context for measurement error relative to the MIC.

Conclusions:

  • MIC values are applied individually but derived from group data, necessitating careful interpretation.
  • Interpreting MIC involves inherent uncertainties that must be acknowledged.
  • Understanding the distribution of change scores is fundamental for appreciating MIC uncertainty in clinical practice and research.