Reasoning about Sampling Distribitions
By B. Chance, R. Mas, Joan Garfield. 2004
124 citations recorded by the source corpus.
Subfields: Artificial Intelligence, History and Philosophy of Science, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/5c0fc53c668f872504492d6d0a2c12a5d3851c7f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Dani Ben-Zvi
- M. Pfannkuch
- S. Vanhoof
- Patrick Onghena
- W. Van den Noortgate
- Ana Elisa Castro Sotos
- Joan Garfield
- Nicholas J. Horton
In History and Philosophy of Science
In Statistics and Probability
- Dani Ben-Zvi
- M. Pfannkuch
- Joan Garfield
- Chris Wild
- Nicholas J. Horton
- M. Regan
- Patrick Onghena
- S. Vanhoof
Citations in the corpus
- Towards more accessible conceptions of statistical inference (2011, background): “Additionally, research strongly suggests (e.g. Chance et al. (2004)) that large numbers of students fail to comprehend formal statistical inference when they do meet it at either school or introductory university level, and that they will continue to do so unless a much better job is done of laying…”
- How Students Learn Statistics Revisited: A Current Review of Research on Teaching and Learning Statistics (2007, background): “Journal compilation C© 2007 International Statistical Institute Ciancetta, 2007), measures of spread such as the standard deviation (delMas & Liu, 2005), and sampling contexts (Chance et al., 2004; Reading & Shaughnessy, 2004; Watson, 2004).”
- Students’ misconceptions of statistical inference: A review of the empirical evidence from research on statistics education (2007, methodology): “We found two studies related to these misconceptions: Lipson (2002) and Chance et al. (2004).”