Pub Date : 2021-10-02DOI: 10.1080/09332480.2021.2003636
Mary Gray, Nimai M. Mehta
The admission of expert opinion by courts meant to assist the trier of facts has enjoyed a checkered history within the Anglo-American legal system. Progress has been achieved where expert testimony proffered was determined by the court to be relevant, material, and competent. Cases where these criteria of admissibility remained undeveloped, or were misapplied in the face of complex evidence, expert testimony has done more harm than good in the search for truth. From Pascal and Fermat to se Moivre, from Bayes to Fisher, probability and data have come together to establish the role of statistics in civil and criminal justice. We explore the role statisticians as expert witnesses have played within the Anglo-American system of justice - in the US courts and in the Indian subcontinent. The evolution of the 1872 Indian Evidence Act has in many ways paralleled the changing rules of evidence and expert testimony in U.S. federal and state statutes. This is evident in the challenges courts in both places have faced, for example, in the application of the Daubert guidelines in cases involving complex, scientific data - in matters of DNA evidence, the environment, public health, etc. Lastly we look at the extent to which the two legal systems have retained the adversarial system as a check on expert opinion and its misuse.
{"title":"Liars, Damned Liars, and …","authors":"Mary Gray, Nimai M. Mehta","doi":"10.1080/09332480.2021.2003636","DOIUrl":"https://doi.org/10.1080/09332480.2021.2003636","url":null,"abstract":"The admission of expert opinion by courts meant to assist the trier of facts has enjoyed a checkered history within the Anglo-American legal system. Progress has been achieved where expert testimony proffered was determined by the court to be relevant, material, and competent. Cases where these criteria of admissibility remained undeveloped, or were misapplied in the face of complex evidence, expert testimony has done more harm than good in the search for truth. From Pascal and Fermat to se Moivre, from Bayes to Fisher, probability and data have come together to establish the role of statistics in civil and criminal justice. We explore the role statisticians as expert witnesses have played within the Anglo-American system of justice - in the US courts and in the Indian subcontinent. The evolution of the 1872 Indian Evidence Act has in many ways paralleled the changing rules of evidence and expert testimony in U.S. federal and state statutes. This is evident in the challenges courts in both places have faced, for example, in the application of the Daubert guidelines in cases involving complex, scientific data - in matters of DNA evidence, the environment, public health, etc. Lastly we look at the extent to which the two legal systems have retained the adversarial system as a check on expert opinion and its misuse.","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"27 1","pages":"23 - 27"},"PeriodicalIF":0.0,"publicationDate":"2021-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80622667","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}
Pub Date : 2021-10-02DOI: 10.1080/09332480.2021.2003641
C. Robert
This article contains book reviews of Poems that Solve Puzzles by Chris Bleakley, Quick Calculations by Trevor Davis Lipscombe, The Error of Truth by Steven Osterling, Bernoulli's Fallacy by Aubrey Clayton, and A History of Data Visualization and Graphic Communication by Michael Friendly and Howard Wainer.
{"title":"Poems that Solve Puzzles","authors":"C. Robert","doi":"10.1080/09332480.2021.2003641","DOIUrl":"https://doi.org/10.1080/09332480.2021.2003641","url":null,"abstract":"This article contains book reviews of Poems that Solve Puzzles by Chris Bleakley, Quick Calculations by Trevor Davis Lipscombe, The Error of Truth by Steven Osterling, Bernoulli's Fallacy by Aubrey Clayton, and A History of Data Visualization and Graphic Communication by Michael Friendly and Howard Wainer.","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"12 1","pages":"36 - 36"},"PeriodicalIF":0.0,"publicationDate":"2021-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87599189","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}
Pub Date : 2021-10-02DOI: 10.1080/09332480.2021.2003640
C. Robert
This article contains book reviews of Poems that Solve Puzzles by Chris Bleakley, Quick Calculations by Trevor Davis Lipscombe, The Error of Truth by Steven Osterling, Bernoulli's Fallacy by Aubrey Clayton, and A History of Data Visualization and Graphic Communication by Michael Friendly and Howard Wainer.
{"title":"The Error of Truth","authors":"C. Robert","doi":"10.1080/09332480.2021.2003640","DOIUrl":"https://doi.org/10.1080/09332480.2021.2003640","url":null,"abstract":"This article contains book reviews of Poems that Solve Puzzles by Chris Bleakley, Quick Calculations by Trevor Davis Lipscombe, The Error of Truth by Steven Osterling, Bernoulli's Fallacy by Aubrey Clayton, and A History of Data Visualization and Graphic Communication by Michael Friendly and Howard Wainer.","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"25 1","pages":"34 - 36"},"PeriodicalIF":0.0,"publicationDate":"2021-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79147292","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}
Pub Date : 2021-07-30DOI: 10.1162/99608f92.f0ad0287
Y. Benjamini, R.D. De Veaux, B. Efron, Scott Evans, Mark Glickman, B. Graubard, Xuming He, X. Meng, N. Reid, S. Stigler, S. Vardeman, C. Wikle, Tommy Wright, Linda J. Young, K. Kafadar
The value of hypothesis testing, and the frequent misinterpretation of p-values as a cornerstone of statistical methodology, continues to be debated. In 2019, the President of the American Statistical Association (ASA) convened a Task Force to write a succinct statement about the use of statistical methods in scientific studies, specifically hypothesis tests and p-values, and their connection to replicability.
{"title":"ASA President’s Task Force Statement on Statistical Significance and Replicability","authors":"Y. Benjamini, R.D. De Veaux, B. Efron, Scott Evans, Mark Glickman, B. Graubard, Xuming He, X. Meng, N. Reid, S. Stigler, S. Vardeman, C. Wikle, Tommy Wright, Linda J. Young, K. Kafadar","doi":"10.1162/99608f92.f0ad0287","DOIUrl":"https://doi.org/10.1162/99608f92.f0ad0287","url":null,"abstract":"The value of hypothesis testing, and the frequent misinterpretation of p-values as a cornerstone of statistical methodology, continues to be debated. In 2019, the President of the American Statistical Association (ASA) convened a Task Force to write a succinct statement about the use of statistical methods in scientific studies, specifically hypothesis tests and p-values, and their connection to replicability.","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"31 1","pages":"10 - 11"},"PeriodicalIF":0.0,"publicationDate":"2021-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77193353","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}
Pub Date : 2021-07-03DOI: 10.1080/09332480.2021.1981053
V. Balinskaite
Statisticians for Society is a scheme funded by the National Lottery Community Fund, connecting charities with volunteers from the Royal Statistical Society (RSS) membership who provide pro bono work. In this article, RSS fellow Dr Violeta Balinskaite describes the pro bono work she did for Sydenham Gardens, a charity that provides group arts and activity-based recovery programmes for people with mental health problems. The charity needed help understanding their data and how to analyse it.
{"title":"An Assessment of the Change in Co-worker Well-being at Sydenham Garden","authors":"V. Balinskaite","doi":"10.1080/09332480.2021.1981053","DOIUrl":"https://doi.org/10.1080/09332480.2021.1981053","url":null,"abstract":"Statisticians for Society is a scheme funded by the National Lottery Community Fund, connecting charities with volunteers from the Royal Statistical Society (RSS) membership who provide pro bono work. In this article, RSS fellow Dr Violeta Balinskaite describes the pro bono work she did for Sydenham Gardens, a charity that provides group arts and activity-based recovery programmes for people with mental health problems. The charity needed help understanding their data and how to analyse it.","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"78 1","pages":"W82 - W85"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78412600","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}
Pub Date : 2021-07-03DOI: 10.1080/09332480.2021.1979808
C. Augustin, I. Holeman, E. Salomon, H. Olsen, Phillip Azar, M. Ayyangar
Abstract Digital tools make it easier to collect data about patients closer to where they live, understand their health needs better, and treat them faster, thereby saving lives. Community Health Workers (CHWs) are increasingly collecting digital data through the course of care delivery. However, despite the widespread championing of digital health tools, the full impact of these technologies has not been realized because CHW-collected data is often considered low quality and unreliable for data-driven decision-making. At a systemic level, mistrust in the quality of the data (a lack of so-called ‘data trust’) limits the potential impact. The primary objective of this research program was to identify inconsistent or problematic data (IoP) occurring across a digitally enabled CHW health system in order to recommend changes in digital health tools and processes that might increase trust in CHW-collected data. In this exploratory study, data were analyzed using a variety of statistical and data science approaches including clustering algorithms, histograms, box plots, and Sankey diagrams. IoP were identified from a suite of 160 tests internally developed to identify IoP at the health platform level. As anticipated, data exhibited issues with accuracy, completeness, and timeliness across digital health forms. While each IoP issue identified could be individually remediated, recommendations provided are centered on a platform-wide (and tool agnostic) approach to data quality in community health.
{"title":"Pathways to Increasing Trust in Public Health Data","authors":"C. Augustin, I. Holeman, E. Salomon, H. Olsen, Phillip Azar, M. Ayyangar","doi":"10.1080/09332480.2021.1979808","DOIUrl":"https://doi.org/10.1080/09332480.2021.1979808","url":null,"abstract":"Abstract Digital tools make it easier to collect data about patients closer to where they live, understand their health needs better, and treat them faster, thereby saving lives. Community Health Workers (CHWs) are increasingly collecting digital data through the course of care delivery. However, despite the widespread championing of digital health tools, the full impact of these technologies has not been realized because CHW-collected data is often considered low quality and unreliable for data-driven decision-making. At a systemic level, mistrust in the quality of the data (a lack of so-called ‘data trust’) limits the potential impact. The primary objective of this research program was to identify inconsistent or problematic data (IoP) occurring across a digitally enabled CHW health system in order to recommend changes in digital health tools and processes that might increase trust in CHW-collected data. In this exploratory study, data were analyzed using a variety of statistical and data science approaches including clustering algorithms, histograms, box plots, and Sankey diagrams. IoP were identified from a suite of 160 tests internally developed to identify IoP at the health platform level. As anticipated, data exhibited issues with accuracy, completeness, and timeliness across digital health forms. While each IoP issue identified could be individually remediated, recommendations provided are centered on a platform-wide (and tool agnostic) approach to data quality in community health.","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"45 1","pages":"24 - 32"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76285317","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}
Pub Date : 2021-07-03DOI: 10.1080/09332480.2021.1979809
Jennifer Unangst
Despite increasing access to non-designed data sources, surveys continue to be an important tool for studying difficult-to-reach populations. In this article, I share highlights from two Statistics without Borders projects where surveys were used to study remote or vulnerable populations. Each project addressed a different part of the survey lifecycle: the design phase versus the analysis phase. These examples illustrate concepts from survey statistics and show how we, as statisticians, can support the mission of organizations who are using surveys to make a positive difference.
{"title":"Surveys for the Public Good: Examples from Statistics Without Borders","authors":"Jennifer Unangst","doi":"10.1080/09332480.2021.1979809","DOIUrl":"https://doi.org/10.1080/09332480.2021.1979809","url":null,"abstract":"Despite increasing access to non-designed data sources, surveys continue to be an important tool for studying difficult-to-reach populations. In this article, I share highlights from two Statistics without Borders projects where surveys were used to study remote or vulnerable populations. Each project addressed a different part of the survey lifecycle: the design phase versus the analysis phase. These examples illustrate concepts from survey statistics and show how we, as statisticians, can support the mission of organizations who are using surveys to make a positive difference.","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"81 1","pages":"33 - 37"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88387322","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}
Pub Date : 2021-07-03DOI: 10.1080/09332480.2021.1979807
O. Olubusoye, O. Akintande, Eric A. Vance
We describe a case study from the LISA 2020 Network of how statisticians produced and analyzed data to create policy recommendations for enhancing election participation in Nigeria. This is an example of how statisticians can collaborate with data producers and data decision makers to transform evidence into action for development.
{"title":"Transforming Evidence to Action: The Case of Election Participation in Nigeria","authors":"O. Olubusoye, O. Akintande, Eric A. Vance","doi":"10.1080/09332480.2021.1979807","DOIUrl":"https://doi.org/10.1080/09332480.2021.1979807","url":null,"abstract":"We describe a case study from the LISA 2020 Network of how statisticians produced and analyzed data to create policy recommendations for enhancing election participation in Nigeria. This is an example of how statisticians can collaborate with data producers and data decision makers to transform evidence into action for development.","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"5 1","pages":"13 - 23"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76079553","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}
Pub Date : 2021-07-03DOI: 10.1080/09332480.2021.1979818
Samantha Cheng, C. Augustin
The rise of the “invest in open” movement has led to increased focus on - and recognition of the positive benefit of - open source toolkits as a way of democratizing access to software that is typically expensive and therefore restricted to research institutions. From 2015-2017, DataKind partnered with researchers from NCEAS through the SNAPP Consortium to tackle a problem common across many sectors: how to digest the amount of evidence available for decision-making in a way that would still allow for timely decisions to be made. Realizing that the evidence was stored primarily in PDFs and that the rise of machine learning techniques such as natural language processing meant that thousands of words from PDFs could be processed on local computers, the research team took an approach of building an open source tool to compete with commercially available evidence synthesis tools. With an algorithmic backend that relies on word vectorization, this project is an example of technology use to aid common labor intensive researcher tasks. From inception to early maintenance, this project produced many valuable lessons regarding the launch and stewardship of a public good and this article is a reflection of the learnings across that process.
{"title":"Keep a Human in the Machine and Other Lessons Learned from Deploying and Maintaining Colandr","authors":"Samantha Cheng, C. Augustin","doi":"10.1080/09332480.2021.1979818","DOIUrl":"https://doi.org/10.1080/09332480.2021.1979818","url":null,"abstract":"The rise of the “invest in open” movement has led to increased focus on - and recognition of the positive benefit of - open source toolkits as a way of democratizing access to software that is typically expensive and therefore restricted to research institutions. From 2015-2017, DataKind partnered with researchers from NCEAS through the SNAPP Consortium to tackle a problem common across many sectors: how to digest the amount of evidence available for decision-making in a way that would still allow for timely decisions to be made. Realizing that the evidence was stored primarily in PDFs and that the rise of machine learning techniques such as natural language processing meant that thousands of words from PDFs could be processed on local computers, the research team took an approach of building an open source tool to compete with commercially available evidence synthesis tools. With an algorithmic backend that relies on word vectorization, this project is an example of technology use to aid common labor intensive researcher tasks. From inception to early maintenance, this project produced many valuable lessons regarding the launch and stewardship of a public good and this article is a reflection of the learnings across that process.","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"20 1","pages":"56 - 60"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75703331","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}
Pub Date : 2021-07-03DOI: 10.1080/09332480.2021.1979814
Blake Gentry, M. Richardson, Diego Piña Lopez, Joseph Watkins
The year 2020 will be remembered in history as the year of the COVID-19 pandemic. This coronavirus outbreak revealed in stark reality vast economic, educational, and health access disparities inside societies that experience systemic racism and endure repeated climate induced catastrophes. If we are to learn from these circumstances, then we must have thoughtfully collected relevant data, especially from the most marginalized populations. Here, we introduce you to the state of affairs of one such population - Indigenous Mayas migrating through the Casa Alitas shelter in Tucson, Arizona. Through the data collected at Casa Alitas and a database maintained by The New York Times, we will investigate the migrants origins in Mexico and Guatemala, their destinations in the United States, and the circumstances of the pandemic at the place of their arrival. We will see that places that provided social services in Mayan languages had substantially lower incidence of COVID-19.
{"title":"Indigenous Language Migration along the U.S. Southwestern Border—the View from Arizona","authors":"Blake Gentry, M. Richardson, Diego Piña Lopez, Joseph Watkins","doi":"10.1080/09332480.2021.1979814","DOIUrl":"https://doi.org/10.1080/09332480.2021.1979814","url":null,"abstract":"The year 2020 will be remembered in history as the year of the COVID-19 pandemic. This coronavirus outbreak revealed in stark reality vast economic, educational, and health access disparities inside societies that experience systemic racism and endure repeated climate induced catastrophes. If we are to learn from these circumstances, then we must have thoughtfully collected relevant data, especially from the most marginalized populations. Here, we introduce you to the state of affairs of one such population - Indigenous Mayas migrating through the Casa Alitas shelter in Tucson, Arizona. Through the data collected at Casa Alitas and a database maintained by The New York Times, we will investigate the migrants origins in Mexico and Guatemala, their destinations in the United States, and the circumstances of the pandemic at the place of their arrival. We will see that places that provided social services in Mayan languages had substantially lower incidence of COVID-19.","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"3 1","pages":"47 - 55"},"PeriodicalIF":0.0,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84528739","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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