the National Science Foundation
Anthony Carpi and
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.
Metadata instance created
October 1, 2010
by Caroline Hall
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)
12. HABITS OF MIND
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.
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>.
Carpi, A., & Egger, A. (2010, September 26). Visionlearning: Data: Uncertainty, Error, and Confidence. Retrieved March 27, 2017, from Visionlearning: http://www.visionlearning.com/en/library/Process-of-Science/49/Uncertainty-Error-and-Confidence/157
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 27 March 2017).
Carpi, Anthony, and Anne Egger. Visionlearning: Data: Uncertainty, Error, and Confidence. Visionlearning, 2000. 26 Sep. 2010. National Science Foundation. 27 Mar. 2017 <http://www.visionlearning.com/en/library/Process-of-Science/49/Uncertainty-Error-and-Confidence/157>.
%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 27 March 2017 %8 September 26, 2010 %9 text/html %U http://www.visionlearning.com/en/library/Process-of-Science/49/Uncertainty-Error-and-Confidence/157
Disclaimer: ComPADRE offers citation styles as a guide only. We cannot offer interpretations about citations as this is an automated procedure. Please refer to the style manuals in the Citation Source Information area for clarifications.
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.