Chance (New York, N.Y.)最新文献

{"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}

{"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}

{"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":"Building and Teaching a Statistics Curriculum for Post-Doctoral Biomedical Scientists at a Free-Standing Cancer Center","authors":"S. Patil, J. Satagopan","doi":"10.1080/09332480.2022.2039036","DOIUrl":"https://doi.org/10.1080/09332480.2022.2039036","url":null,"abstract":"In the past decade, the cancer research community has made considerable progress in characterizing the genomic features of human tumors. Knowledge of molecular drivers of cancer has therefore increased greatly. Although the oncology community hoped that this would lead to more effective therapies, our ability to translate laboratory cancer research into clinical success has been remarkably low –only 5% of the agents demonstrated to have anticancer activity in laboratory studies went on to achieve success in phase III clinical trials. Many factors are responsible for this high percentage of failures. In addition to the biological difficulties of generalizing lab animal treatments to humans, other factors involved in driving this low rate include misuse or misunderstanding of statistical design and analysis concepts, interpretation and reporting methods. Recognizing the seriousness of these widely prevalent and correctable issues, the National Institutes of Health has called for the full engagement of the entire biomedical research enterprise to implement the resources needed to improve and sustain the statistical rigor and successful translation of preclinical cancer research. To this end, we developed a statistics curriculum for early-career preclinical cancer researchers (i.e., postdoctoral researchers) conducting laboratory research at Memorial Sloan Kettering Cancer Center as well as the broader research community. Our proposed curriculum was delivered over an eight-week period with one 60-90-minute class per week and included hands-on activities with experimental designs, data analysis and participatory dialogues. We developed an evaluation of the curriculum and have plans to make all resources available to the broader statistics and cancer research communities. In this column, we discuss our efforts to build such a statistics curriculum for post-doctoral biomedical scientists at a free-standing cancer center. We share experiences in the development of the curriculum and provide insights from the perspective of students, their laboratory leads, and collaborative biostatisticians.","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"100 1","pages":"56 - 64"},"PeriodicalIF":0.0,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77364160","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}