Jake M. Hofman, Daniel G. Goldstein, Jessica Hullman. How Visualizing Inferential Uncertainty Can Mislead Readers About Treatment Effects in Scientific Results. In Regina Bernhaupt, Florian 'Floyd' Mueller, David Verweij, Josh Andres, Joanna McGrenere, Andy Cockburn, Ignacio Avellino, Alix Goguey, Pernille Bjøn, Shengdong Zhao, Briane Paul Samson, Rafal Kocielnik, editors, CHI '20: CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, April 25-30, 2020. pages 1-12, ACM, 2020. [doi]
@inproceedings{HofmanGH20,
title = {How Visualizing Inferential Uncertainty Can Mislead Readers About Treatment Effects in Scientific Results},
author = {Jake M. Hofman and Daniel G. Goldstein and Jessica Hullman},
year = {2020},
doi = {10.1145/3313831.3376454},
url = {https://doi.org/10.1145/3313831.3376454},
researchr = {https://researchr.org/publication/HofmanGH20},
cites = {0},
citedby = {0},
pages = {1-12},
booktitle = {CHI '20: CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, April 25-30, 2020},
editor = {Regina Bernhaupt and Florian 'Floyd' Mueller and David Verweij and Josh Andres and Joanna McGrenere and Andy Cockburn and Ignacio Avellino and Alix Goguey and Pernille Bjøn and Shengdong Zhao and Briane Paul Samson and Rafal Kocielnik},
publisher = {ACM},
isbn = {978-1-4503-6708-0},
}