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Related Concept Videos

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.
Random and Systematic Errors01:20

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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Random and Systematic Errors01:20

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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.
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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
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In operant conditioning, the timing of reinforcement is crucial. For animals like rats and cats, immediate reinforcement (within a few seconds) is much more effective than delayed reinforcement. For example, a food reward for a rat needs to follow within 30 seconds of pressing a bar to be effective. 
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Does Timing Matter? Exploring the Effects of Measurement Error on Models.

Brock D Sherlock1,2, Marko A A Boon2, Maria Vlasiou3

  • 1School of Mathematics & Statistics, University of New South Wales, Sydney, 2052, NSW, Australia.

Bulletin of Mathematical Biology
|May 19, 2026
PubMed
Summary
This summary is machine-generated.

Measurement error in independent variables, especially time, can bias parameter inference in biological models. Researchers should assess data and consider statistical methods to correct for these errors, particularly in sensitive systems like oscillating models.

Keywords:
Data FittingError in VariablesMathematical ModellingMeasurement ErrorParameter inference

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Area of Science:

  • Mathematical Biology
  • Statistical Modeling
  • Experimental Data Analysis

Background:

  • Measurement error is inherent in experimental data collection, commonly affecting dependent variables.
  • Errors in independent variables, particularly time, are less studied but crucial in biological modeling.
  • Accurate parameter inference is vital for understanding biological systems.

Purpose of the Study:

  • To investigate the impact of independent variable measurement error on parameter inference in biological sciences.
  • To evaluate statistical methods for correcting measurement errors in biological modeling.
  • To highlight the importance of assessing data for measurement error in the modeling workflow.

Main Methods:

  • Simulated data studies were used to examine various independent variable error models.
  • Focus was placed on errors in the timing of measurements.
  • Review and assessment of statistical methods for measurement error correction in a biological context.

Main Results:

  • Parameter inference is often robust to independent variable measurement errors, even without explicit correction.
  • Oscillating biological systems are particularly sensitive, leading to biased parameter estimates.
  • Measurement errors can have unexpected and significant effects on model parameterization.

Conclusions:

  • Independent variable measurement error can non-trivially impact biological model parameter inference.
  • Statistical methods for error correction are available but require careful assessment of applicability.
  • Evaluating data for potential measurement errors should be an integral part of the biological modeling process.