Publications by Year: 2021

2021
Cluster Analysis of Participant Responses for Test Generation or Teaching (2nd)
Gary King, Brian Lukoff, and Eric Mazur. 2/16/2021. “Cluster Analysis of Participant Responses for Test Generation or Teaching (2nd).” United States of America US 10,922,991 B2 (U.S Patent and Trademark Office).Abstract
Textual responses to open-ended (i.e., free-response) items provided by participants (e.g., by means of mobile wireless devices) are automatically classified, enabling an instructor to assess the responses in a convenient, organized fashion and adjust instruction accordingly.
patent
Participant Grouping for Enhanced Interactive Experience (4th)
Gary King, Brian Lukoff, and Eric Mazur. 1/26/2021. “Participant Grouping for Enhanced Interactive Experience (4th).” United States of America 10,902,031 B2 (U.S Patent and Trademark Office).Abstract
Representative embodiments of a method for grouping participants in an activity include the steps of: (i) defining a grouping policy; (ii) storing, in a database, participant records that include a participant identifier, a characteristic associated with the participant, and/or an identifier for a participant's handheld device; (iii) defining groupings based on the policy and characteristics of the participants relating to the policy and to the activity; and (iv) communicating the groupings to the handheld devices to establish the groups.
Patent
A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results
Beau Coker, Cynthia Rudin, and Gary King. 2021. “A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results.” Management Science, Pp. 1-24. Publisher's VersionAbstract
Inference is the process of using facts we know to learn about facts we do not know. A theory of inference gives assumptions necessary to get from the former to the latter, along with a definition for and summary of the resulting uncertainty. Any one theory of inference is neither right nor wrong, but merely an axiom that may or may not be useful. Each of the many diverse theories of inference can be valuable for certain applications. However, no existing theory of inference addresses the tendency to choose, from the range of plausible data analysis specifications consistent with prior evidence, those that inadvertently favor one's own hypotheses. Since the biases from these choices are a growing concern across scientific fields, and in a sense the reason the scientific community was invented in the first place, we introduce a new theory of inference designed to address this critical problem. We derive "hacking intervals," which are the range of a summary statistic one may obtain given a class of possible endogenous manipulations of the data. Hacking intervals require no appeal to hypothetical data sets drawn from imaginary superpopulations. A scientific result with a small hacking interval is more robust to researcher manipulation than one with a larger interval, and is often easier to interpret than a classical confidence interval. Some versions of hacking intervals turn out to be equivalent to classical confidence intervals, which means they may also provide a more intuitive and potentially more useful interpretation of classical confidence intervals. 
Article