Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Uncertainty in Measurement: Accuracy and Precision

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: Overview

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.

Uncertainty in Measurement: Reading Instruments

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...

Propagation of Uncertainty from Random Error

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...

Significant Figures in Calculations

Significant Figures in Calculations

Uncertainty in measurements can be avoided by reporting the results of a calculation with the correct number of significant figures. This can be determined by the following rules for rounding numbers:

Uncertainty in Measurement: Significant Figures

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.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Statistical and Structural Bias in Birth-Death Models.

Bulletin of mathematical biology·2026

Same author

Longevity in plants impacts phylogenetic and population dynamics.

The New phytologist·2025

Same author

Longevity in plants impacts phylogenetic and population dynamics.

bioRxiv : the preprint server for biology·2025

Same author

Phylogenomics reveals the slow-burning fuse of diatom evolution.

Proceedings of the National Academy of Sciences of the United States of America·2025

Same author

The macroevolutionary dynamics of pharyngognathy in fishes fail to support the key innovation hypothesis.

Nature communications·2024

Same author

Transitions Into Freezing Environments Linked With Shifts in Phylogenetic Integration Between Vitaceae Leaf Traits.

Ecology and evolution·2024

Search research articles

Related Experiment Video

Updated: May 16, 2025

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

Navigating "tip fog": embracing uncertainty in tip measurements.

Jeremy M Beaulieu¹, Brian C O'Meara²

¹Department of Biological Sciences, University of Arkansas, Fayetteville, AR, United States.

Evolution; International Journal of Organic Evolution

|April 3, 2025

Summary

Ignoring "tip fog," or variation in evolutionary data, leads to inaccurate evolutionary models and biased rate estimates. Accounting for this variance is crucial for precise phylogenetic comparative analyses.

Keywords:

evolutionary rates hidden Markov model intraspecific variation macroevolution measurement error tip fog

More Related Videos

Magnetic Tweezers for the Measurement of Twist and Torque

Magnetic Tweezers for the Measurement of Twist and Torque

Published on: May 19, 2014

Atomic Force Microscopy Cantilever-Based Nanoindentation: Mechanical Property Measurements at the Nanoscale in Air and Fluid

Atomic Force Microscopy Cantilever-Based Nanoindentation: Mechanical Property Measurements at the Nanoscale in Air and Fluid

Published on: December 2, 2022

Related Experiment Videos

Last Updated: May 16, 2025

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

Magnetic Tweezers for the Measurement of Twist and Torque

Magnetic Tweezers for the Measurement of Twist and Torque

Published on: May 19, 2014

Atomic Force Microscopy Cantilever-Based Nanoindentation: Mechanical Property Measurements at the Nanoscale in Air and Fluid

Atomic Force Microscopy Cantilever-Based Nanoindentation: Mechanical Property Measurements at the Nanoscale in Air and Fluid

Published on: December 2, 2022

Area of Science:

Evolutionary Biology
Phylogenetics
Quantitative Biology

Background:

Evolutionary processes generate inherent variation, crucial for natural selection.
Overlooking variation in biological data leads to inaccurate models and parameter estimates.
Uncertainty in biological data extends beyond measurement error, including various sources of variance.

Purpose of the Study:

To introduce and define "tip fog" as the variance between modeled evolutionary processes and recorded data.
To demonstrate the critical impact of tip fog on comparative models in evolutionary biology.
To provide methods for estimating tip fog and assess its importance in phylogenetic analyses.

Main Methods:

Redevelopment of methods for estimating variance associated with tip fog.
Simulations used to assess the feasibility and significance of tip fog estimation.
Analysis of the impact of tip fog on continuous, discrete, and diversification models.

Main Results:

Neglecting tip fog significantly biases estimates of evolutionary rates.
Higher levels of tip fog result in greater inaccuracies in rate estimations.
Tip fog influences the selection of appropriate evolutionary models.

Conclusions:

Accounting for tip fog is essential for accurate evolutionary rate estimation.
Model selection in phylogenetics is critically affected by the presence of tip fog.
Addressing tip fog improves the reliability of comparative methods in evolutionary biology.