{"title":"Correspondence Analysis Visualization","authors":"Nguyen-Khang Pham, J. Chauchat, J. Dumais","doi":"10.1080/09332480.2022.2066423","DOIUrl":"https://doi.org/10.1080/09332480.2022.2066423","url":null,"abstract":"","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"24 1","pages":"50 - 52"},"PeriodicalIF":0.0,"publicationDate":"2022-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75037088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Mega Millions Lottery and Hypothesis Testing","authors":"M. Orkin","doi":"10.1080/09332480.2022.2066412","DOIUrl":"https://doi.org/10.1080/09332480.2022.2066412","url":null,"abstract":"The Mega Millions Lottery is a game of chance, no skill possible. It’s played in most states and offers multi-million-dollar jackpots. Despite various popular lottery “strategies,” like lucky numbers, birthdays, anniversaries, astrology, studying past results, and so on, there is no skill in buying lottery tickets. This is because lottery winners are determined by picking numbers at random. If you think that the configuration of the planets or a pattern you saw in your oatmeal this morning has any effect on your chance of winning, then maybe you have apophenia, the tendency to perceive a connection between unrelated or random things. The chance of winning the jackpot is so low that if you buy a ticket, you will almost certainly lose. This doesn’t mean that everybody will lose. We will look at lottery results in the context of hypothesis testing and p-values.","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"35 1","pages":"25 - 28"},"PeriodicalIF":0.0,"publicationDate":"2022-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90746776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Struggle for Equal Pay, the Lament of a Female Statistician","authors":"M. Gray","doi":"10.1080/09332480.2022.2066413","DOIUrl":"https://doi.org/10.1080/09332480.2022.2066413","url":null,"abstract":"There are many laws, state and federal, prohibiting discrimination in employment on the basis of protected categories – race, gender, national origin, age, disability. What is not so clear is what kind of discrimination is unlawful. Is it only disparate treatment discrimination, where one is denied a benefit because of her/his protected status? Or is it also disparate impact discrimination, where a facially neutral rule or practice has a disparate impact on members of a protected class? The latter category has provided ample engagement of statisticians whose evidence the courts must decide demonstrates sufficient disparity to constitute discrimination or not, as well as demonstrating that the impactful criterion is not really necessary for the position in question. In the seminal disparate impact case, it might have been easy to show that a high school diploma was not needed to be a lineman for a power company, but what about student evaluations for a faculty position?","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"58 1","pages":"29 - 31"},"PeriodicalIF":0.0,"publicationDate":"2022-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74054123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Exploring COVID Data with Benford’s and Zipf’s Laws","authors":"P. Velleman, H. Wainer","doi":"10.1080/09332480.2022.2066410","DOIUrl":"https://doi.org/10.1080/09332480.2022.2066410","url":null,"abstract":"John von Neumann emphasized the importance of models in science. In this paper we compare the efficacy and ease of use of two quite different models, Benford’s Law and a version of Zipf’s Law to help us to understand the data that have rained upon us from the COVID pandemic. We conclude that Zipf’s Law seems to have much to offer. We recommend it and urge others to try it out. Benford’s Law, not so much.","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"13 1","pages":"11 - 15"},"PeriodicalIF":0.0,"publicationDate":"2022-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91153295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nicholas J. Horton, Jie Chao, William Finzer, Phebe Palmer
{"title":"Spam Four Ways: Making Sense of Text Data","authors":"Nicholas J. Horton, Jie Chao, William Finzer, Phebe Palmer","doi":"10.1080/09332480.2022.2066414","DOIUrl":"https://doi.org/10.1080/09332480.2022.2066414","url":null,"abstract":"The world is full of text data, yet text analytics has not traditionally played a large part in statistics education. We consider four different ways to provide students with opportunities to explore whether email messages are unwanted correspondence (spam). Text from subject lines are used to identify features that can be used in classification. The approaches include use of a Model Eliciting Activity, exploration with CODAP, modeling with a specially designed Shiny app, and coding more sophisticated analyses using R. The approaches vary in their use of technology and code but all share the common goal of using data to make better decisions and assessment of the accuracy of those decisions.","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"48 1","pages":"32 - 40"},"PeriodicalIF":0.0,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73780548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Skewed Distributions in Data Science","authors":"N. Dasgupta","doi":"10.1080/09332480.2022.2039034","DOIUrl":"https://doi.org/10.1080/09332480.2022.2039034","url":null,"abstract":"This column is about raising questions, rather than providing answers. These days “data based decision making” is the rage among administrators in both industry and academia. The desire for this dependence on algorithms stems from the general idea that “humans are biased but machines are not”. More and more social decisions, like qualifying for welfare are, are made using algorithms. With this, the data scientists, who are behind the algorithms, are given a lot of power (and responsibility.) In this column, I discuss demographic characteristics of data scientists with conjectures on why this group is non-diverse. Should we allow a small group of non-representative people to make decisions that affect affect larger society?","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"22 1","pages":"51 - 55"},"PeriodicalIF":0.0,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86191134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hollylynne S. Lee, Z. Vaskalis, David J. Stokes, Taylor Harrison
{"title":"A Look into the AP Statistics Classroom: Who Teaches It and What Aspects of Statistics Do They Emphasize?","authors":"Hollylynne S. Lee, Z. Vaskalis, David J. Stokes, Taylor Harrison","doi":"10.1080/09332480.2022.2039028","DOIUrl":"https://doi.org/10.1080/09332480.2022.2039028","url":null,"abstract":"With Advanced Placement (AP) Statistics continuing to grow in enrollment and its importance as an optional course in high school, we aimed to understand more about the practices in this course. From a survey of 445 AP Statistics teachers, and interviews with 18 volunteers, we offer insight into the teachers of this course, what their classrooms look like, and the aspects of statistics that are emphasized in their curriculum and instruction. Results can assist those in the statistics education community who work with AP Statistics teachers on a local, regional, or national level.","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"60 1","pages":"38 - 47"},"PeriodicalIF":0.0,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79563595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Why Some People Don’t Listen to Statisticians","authors":"A. Paller","doi":"10.1080/09332480.2022.2039033","DOIUrl":"https://doi.org/10.1080/09332480.2022.2039033","url":null,"abstract":"Effective visuals can make or break a presentation. In this article, we discuss how to successfully communicate your ideas with the use of visuals.","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"409 1","pages":"48 - 50"},"PeriodicalIF":0.0,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76494535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Mathematical Anatomy of the Gambler’s Fallacy","authors":"Steven Tijms","doi":"10.1080/09332480.2022.2038998","DOIUrl":"https://doi.org/10.1080/09332480.2022.2038998","url":null,"abstract":"The classic explanation of the gambler's fallacy, proposed exactly fifty years ago by Amos Tversky and Daniel Kahneman, describes the fallacy as a cognitive bias resulting from the psychological makeup of human judgment. We will show that the gambler's fallacy is not in fact a psychological phenomenon, but has its roots in the counter-intuitive mathematics of chance.","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"86 4 1","pages":"11 - 17"},"PeriodicalIF":0.0,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91117222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Approaches to a Dilemma During the Pandemic: Sequential Successes and Simultaneous Successes","authors":"Steven B. Kim, Joonghak Lee","doi":"10.1080/09332480.2022.2039026","DOIUrl":"https://doi.org/10.1080/09332480.2022.2039026","url":null,"abstract":"Since the breakout of the COVID-19 pandemic, many countries and local-level governments have made and adjusted decisions to control the movement of people. It is a dilemma because a decision from the perspective of public health and a decision from the perspective of economy (or freedom of people) are too distant on the spectrum of the level of restriction. Yet, some decision makers seek a compromising decision in order to succeed in both public health and economy. Under five assumptions with simple logistic models, we demonstrate hypothetical scenarios for the probability of simultaneous successes (both public health and economy) and the probability of simultaneous failures. The take-home messages are not surprising: We probably want to solve our pandemic crisis sequentially rather than simultaneously, and our responsible actions can lift the probability of simultaneous successes if we desperately need a compromising decision to solve it simultaneously.","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"46 1","pages":"34 - 37"},"PeriodicalIF":0.0,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89781529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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