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published by the Visionlearning
supported by the National Science Foundation
written by Anthony Carpi and Anne Egger
This is an instructional module designed to introduce learners to the concepts of uncertainty in experimental research, random error, and systematic error. The authors use a contextual approach, with multiple examples drawn from real life. For example, "accuracy" and "precision" are defined from the context of a biathlon competition. Statistical error is presented within a vignette of W.F. Libby's 1946 experiments on carbon-14 dating. Systematic error is described through a 1993 experiment by Edward Lorenz on mathematical modeling for predicting weather.

Editor's Note: This item includes test questions and links to external resources. The Physics Front recommends it for high school students, undergraduates, and teachers of K-8 science.

This resource is part of Visionlearning, an award-winning set of classroom-tested modules for science education.
Subjects Levels Resource Types
Education Foundations
- Research Design & Methodology
Education Practices
- Active Learning
= Modeling
General Physics
- Measurement/Units
= Error
Mathematical Tools
- Statistics
- High School
- Middle School
- Lower Undergraduate
- Instructional Material
= Tutorial
- Assessment Material
= Test
- Dataset
- Audio/Visual
= Image/Image Set
Appropriate Courses Categories Ratings
- Physical Science
- Physics First
- Conceptual Physics
- Algebra-based Physics
- AP Physics
- Activity
- Assessment
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© 2000 visionlearning.com, 2000, http://www.visionlearning.com/docs/terms.php#copyright
classroom-tested, error, evidence-based lessons, evidence-based resources, experiment, experimental design, research methodology, science modules, scientific method, scientific process, uncertainty, validity
Record Cloner:
Metadata instance created October 1, 2010 by Caroline Hall
Record Updated:
August 4, 2016 by Lyle Barbato
Last Update
when Cataloged:
September 26, 2010

AAAS Benchmark Alignments (2008 Version)

1. The Nature of Science

1B. Scientific Inquiry
  • 3-5: 1B/E4. Scientists do not pay much attention to claims about how something they know about works unless the claims are backed up with evidence that can be confirmed, along with a logical argument.
1C. The Scientific Enterprise
  • 3-5: 1C/E2. Clear communication is an essential part of doing science. It enables scientists to inform others about their work, expose their ideas to criticism by other scientists, and stay informed about scientific discoveries around the world.
  • 6-8: 1C/M7. Accurate record-keeping, openness, and replication are essential for maintaining an investigator's credibility with other scientists and society.

12. Habits of Mind

12B. Computation and Estimation
  • 6-8: 12B/M4. Find the mean, median, and mode of a set of data.
  • 6-8: 12B/M8. Decide what degree of precision is adequate and round off the result of calculator operations to enough significant figures to reasonably reflect those of the inputs.
  • 9-12: 12B/H5. Compare data for two groups by representing their averages and spreads graphically.
12D. Communication Skills
  • 9-12: 12D/H3. Choose appropriate summary statistics to describe group differences, always indicating the spread of the data as well as the data's central tendencies.
12E. Critical-Response Skills
  • 9-12: 12E/H1. Notice and criticize claims based on the faulty, incomplete, or misleading use of numbers, such as in instances when (1) average results are reported but not the amount of variation around the average, (2) a percentage or fraction is given but not the total sample size, (3) absolute and proportional quantities are mixed, or (4) results are reported with overstated precision.

AAAS Benchmark Alignments (1993 Version)


B. Computation and Estimation
  • 12B (9-12) #4.  Use computer spreadsheet, graphing, and database programs to assist in quantitative analysis.
  • 12B (9-12) #9.  Consider the possible effects of measurement errors on calculations.
E. Critical-Response Skills
  • 12E (9-12) #6.  Suggest alternative ways of explaining data and criticize arguments in which data, explanations, or conclusions are represented as the only ones worth consideration, with no mention of other possibilities. Similarly, suggest alternative trade-offs in decisions and designs and criticize those in which major trade-offs are not acknowledged.
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Record Link
AIP Format
A. Carpi and A. Egger, (Visionlearning, 2000), WWW Document, (http://www.visionlearning.com/en/library/Process-of-Science/49/Uncertainty-Error-and-Confidence/157).
A. Carpi and A. Egger, Visionlearning: Data: Uncertainty, Error, and Confidence, (Visionlearning, 2000), <http://www.visionlearning.com/en/library/Process-of-Science/49/Uncertainty-Error-and-Confidence/157>.
APA Format
Carpi, A., & Egger, A. (2010, September 26). Visionlearning: Data: Uncertainty, Error, and Confidence. Retrieved May 28, 2017, from Visionlearning: http://www.visionlearning.com/en/library/Process-of-Science/49/Uncertainty-Error-and-Confidence/157
Chicago Format
Carpi, Anthony, and Anne Egger. Visionlearning: Data: Uncertainty, Error, and Confidence. Visionlearning, September 26, 2010. http://www.visionlearning.com/en/library/Process-of-Science/49/Uncertainty-Error-and-Confidence/157 (accessed 28 May 2017).
MLA Format
Carpi, Anthony, and Anne Egger. Visionlearning: Data: Uncertainty, Error, and Confidence. Visionlearning, 2000. 26 Sep. 2010. National Science Foundation. 28 May 2017 <http://www.visionlearning.com/en/library/Process-of-Science/49/Uncertainty-Error-and-Confidence/157>.
BibTeX Export Format
@misc{ Author = "Anthony Carpi and Anne Egger", Title = {Visionlearning: Data: Uncertainty, Error, and Confidence}, Publisher = {Visionlearning}, Volume = {2017}, Number = {28 May 2017}, Month = {September 26, 2010}, Year = {2000} }
Refer Export Format

%A Anthony Carpi
%A Anne Egger
%T Visionlearning: Data: Uncertainty, Error, and Confidence
%D September 26, 2010
%I Visionlearning
%U http://www.visionlearning.com/en/library/Process-of-Science/49/Uncertainty-Error-and-Confidence/157
%O text/html

EndNote Export Format

%0 Electronic Source
%A Carpi, Anthony
%A Egger, Anne
%D September 26, 2010
%T Visionlearning: Data: Uncertainty, Error, and Confidence
%I Visionlearning
%V 2017
%N 28 May 2017
%8 September 26, 2010
%9 text/html
%U http://www.visionlearning.com/en/library/Process-of-Science/49/Uncertainty-Error-and-Confidence/157

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Citation Source Information

The AIP Style presented is based on information from the AIP Style Manual.

The APA Style presented is based on information from APA Style.org: Electronic References.

The Chicago Style presented is based on information from Examples of Chicago-Style Documentation.

The MLA Style presented is based on information from the MLA FAQ.

Visionlearning: Data: Uncertainty, Error, and Confidence:

Is Part Of Visionlearning: The Process of Science

This is the full instructional unit by Visionlearning, The Process of Science. It contains 15 sections, including research methodologies, data collection/analysis, error and uncertainty, scientific ethics, understanding scientific articles, and more.

relation by Caroline Hall

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