Pub Date : 2019-01-01DOI: 10.1080/2330443X.2019.1574686
R. Amin, Alexander Bohnert, David L. Banks
ABSTRACT This study identifies pediatric cancer clusters in Florida for the years 2000–2015. Unlike previous publications on pediatric cancers in Florida, it draws upon an Environmental Protection Agency dataset on carcinogenic air pollution, the National Air Toxics Assessment, as well as more customary demographic variables (age, sex, race). The focus is upon the three most widely seen pediatric cancer types in the USA: brain tumors, leukemia, and lymphomas. The covariates are used in a Poisson regression to predict cancer incidence. The adjusted cluster analysis quantifies the role of each covariate. Using Florida Association of Pediatric Tumor Programs data for 2000–2015, we find statistically significant pediatric cancer clusters, but we cannot associate air pollution with the cancer incidence. Supplementary materials for this article are available online.
{"title":"Patterns of Pediatric Cancers in Florida: 2000–2015","authors":"R. Amin, Alexander Bohnert, David L. Banks","doi":"10.1080/2330443X.2019.1574686","DOIUrl":"https://doi.org/10.1080/2330443X.2019.1574686","url":null,"abstract":"ABSTRACT This study identifies pediatric cancer clusters in Florida for the years 2000–2015. Unlike previous publications on pediatric cancers in Florida, it draws upon an Environmental Protection Agency dataset on carcinogenic air pollution, the National Air Toxics Assessment, as well as more customary demographic variables (age, sex, race). The focus is upon the three most widely seen pediatric cancer types in the USA: brain tumors, leukemia, and lymphomas. The covariates are used in a Poisson regression to predict cancer incidence. The adjusted cluster analysis quantifies the role of each covariate. Using Florida Association of Pediatric Tumor Programs data for 2000–2015, we find statistically significant pediatric cancer clusters, but we cannot associate air pollution with the cancer incidence. Supplementary materials for this article are available online.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2019.1574686","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42238617","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 : 2019-01-01DOI: 10.1080/2330443X.2018.1555068
J. Nguyen, C. Tiu, J. Stewart, David H. Miller
Abstract Mixed-effects models were used to evaluate the global zoning concept using residue data from a comprehensive database of supervised field trials performed in various countries and regions on a variety of pesticide–crop combinations. No statistically significant systematic differences in pesticide residues were found between zones among the pesticide uses examined. In addition, we conducted a simulation to assess the impact of using regional versus global datasets for calculating maximum residue limits (MRLs). The conclusion of this assessment supports the concept of exchangeability of pesticide residue values across geographic regions and opens the possibility of improving harmonization of pesticide regulatory standards by establishing more globally aligned MRLs. Supplemental material for this article is available online.
{"title":"Global Zoning and Exchangeability of Field Trial Residues Between Zones: Are There Systematic Differences in Pesticide Residues Across Geographies?","authors":"J. Nguyen, C. Tiu, J. Stewart, David H. Miller","doi":"10.1080/2330443X.2018.1555068","DOIUrl":"https://doi.org/10.1080/2330443X.2018.1555068","url":null,"abstract":"Abstract Mixed-effects models were used to evaluate the global zoning concept using residue data from a comprehensive database of supervised field trials performed in various countries and regions on a variety of pesticide–crop combinations. No statistically significant systematic differences in pesticide residues were found between zones among the pesticide uses examined. In addition, we conducted a simulation to assess the impact of using regional versus global datasets for calculating maximum residue limits (MRLs). The conclusion of this assessment supports the concept of exchangeability of pesticide residue values across geographic regions and opens the possibility of improving harmonization of pesticide regulatory standards by establishing more globally aligned MRLs. Supplemental material for this article is available online.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2018.1555068","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47736208","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 : 2019-01-01DOI: 10.1080/2330443x.2019.1688740
D. Hu, A. Cooper, Neel Desai, Sophie Guo, Steven Shi, David L. Banks
Abstract Policy-makers should perform a cost-benefit analysis before initiating a war. This article describes a methodology for such assessment, and applies it post hoc to five military actions undertaken by the United States between 1950 and 2000 (the Korean War, the Vietnam War, the invasion of Grenada, the invasion of Panama, and the First Gulf War). The analysis identifies three broad categories of value: human capital, economic outcomes, and national influence. Different stakeholders (politicians, generals, industry, etc.) may assign different weights to these three categories, so this analysis tabulates each separately, and then, as may sometimes be necessary, monetizes them for unified comparison.
{"title":"Cost-Benefit Analysis of Discretionary Wars","authors":"D. Hu, A. Cooper, Neel Desai, Sophie Guo, Steven Shi, David L. Banks","doi":"10.1080/2330443x.2019.1688740","DOIUrl":"https://doi.org/10.1080/2330443x.2019.1688740","url":null,"abstract":"Abstract Policy-makers should perform a cost-benefit analysis before initiating a war. This article describes a methodology for such assessment, and applies it post hoc to five military actions undertaken by the United States between 1950 and 2000 (the Korean War, the Vietnam War, the invasion of Grenada, the invasion of Panama, and the First Gulf War). The analysis identifies three broad categories of value: human capital, economic outcomes, and national influence. Different stakeholders (politicians, generals, industry, etc.) may assign different weights to these three categories, so this analysis tabulates each separately, and then, as may sometimes be necessary, monetizes them for unified comparison.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443x.2019.1688740","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44198259","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 : 2019-01-01DOI: 10.1080/2330443x.2019.1660285
Yi-jie Tang, Nicole M. Dalzell
ABSTRACT Social media and other online sites are being increasingly scrutinized as platforms for cyberbullying and hate speech. Many machine learning algorithms, such as support vector machines, have been adopted to create classification tools to identify and potentially filter patterns of negative speech. While effective for prediction, these methodologies yield models that are difficult to interpret. In addition, many studies focus on classifying comments as either negative or neutral, rather than further separating negative comments into subcategories. To address both of these concerns, we introduce a two-stage model for classifying text. With this model, we illustrate the use of internal lexicons, collections of words generated from a pre-classified training dataset of comments that are specific to several subcategories of negative comments. In the first stage, a machine learning algorithm classifies each comment as negative or neutral, or more generally target or nontarget. The second stage of model building leverages the internal lexicons (called L2CLs) to create features specific to each subcategory. These features, along with others, are then used in a random forest model to classify the comments into the subcategories of interest. We demonstrate our approach using two sets of data. Supplementary materials for this article are available online.
{"title":"Classifying Hate Speech Using a Two-Layer Model","authors":"Yi-jie Tang, Nicole M. Dalzell","doi":"10.1080/2330443x.2019.1660285","DOIUrl":"https://doi.org/10.1080/2330443x.2019.1660285","url":null,"abstract":"ABSTRACT Social media and other online sites are being increasingly scrutinized as platforms for cyberbullying and hate speech. Many machine learning algorithms, such as support vector machines, have been adopted to create classification tools to identify and potentially filter patterns of negative speech. While effective for prediction, these methodologies yield models that are difficult to interpret. In addition, many studies focus on classifying comments as either negative or neutral, rather than further separating negative comments into subcategories. To address both of these concerns, we introduce a two-stage model for classifying text. With this model, we illustrate the use of internal lexicons, collections of words generated from a pre-classified training dataset of comments that are specific to several subcategories of negative comments. In the first stage, a machine learning algorithm classifies each comment as negative or neutral, or more generally target or nontarget. The second stage of model building leverages the internal lexicons (called L2CLs) to create features specific to each subcategory. These features, along with others, are then used in a random forest model to classify the comments into the subcategories of interest. We demonstrate our approach using two sets of data. Supplementary materials for this article are available online.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443x.2019.1660285","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41530975","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 : 2019-01-01DOI: 10.1080/2330443X.2019.1615396
M. Chikina, A. Frieze, W. Pegden
Abstract The article of Cho and Rubinstein-Salzedo seeks to cast doubt on our previous paper, which described a rigorous statistical test which can be applied to reversible Markov chains. In particular, Cho and Rubinstein-Salzedo seem to suggest that the test we describe might not be a reliable indicator of gerrymandering, when the test is applied to certain redistricting Markov chains. However, the examples constructed by Cho and Rubinstein-Salzedo in fact demonstrate a different point: that our test is not the same as another class of gerrymandering tests, which Cho and Rubinstein-Salzedo prefer. But we agree and emphasized this very distinction in our original paper. In this reply, we reply to the criticisms of Cho and Rubinstein-Salzedo, and discuss, more generally, the advantages of the various tests available in the context of detecting gerrymandering of political districtings.
{"title":"Understanding Our Markov Chain Significance Test: A Reply to Cho and Rubinstein-Salzedo","authors":"M. Chikina, A. Frieze, W. Pegden","doi":"10.1080/2330443X.2019.1615396","DOIUrl":"https://doi.org/10.1080/2330443X.2019.1615396","url":null,"abstract":"Abstract The article of Cho and Rubinstein-Salzedo seeks to cast doubt on our previous paper, which described a rigorous statistical test which can be applied to reversible Markov chains. In particular, Cho and Rubinstein-Salzedo seem to suggest that the test we describe might not be a reliable indicator of gerrymandering, when the test is applied to certain redistricting Markov chains. However, the examples constructed by Cho and Rubinstein-Salzedo in fact demonstrate a different point: that our test is not the same as another class of gerrymandering tests, which Cho and Rubinstein-Salzedo prefer. But we agree and emphasized this very distinction in our original paper. In this reply, we reply to the criticisms of Cho and Rubinstein-Salzedo, and discuss, more generally, the advantages of the various tests available in the context of detecting gerrymandering of political districtings.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2019.1615396","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41320895","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 : 2019-01-01DOI: 10.1080/2330443x.2019.1688741
David L. Banks
Dr. Jonathan Ratner’s discussion is amazing and a valuable commentary (and sometimes a corrective) upon the work in our article. We are grateful for his thoughtful examination and testing of the assumptions and methodology we have used. His contribution goes far beyond a typical discussion and is an article in its own right, or at the very least a provocative essay. He makes many important points and builds out our reasoning and expands its scope in numerous ways. This response attempts to briefly address some of the key points and suggestions that he makes. Dr. Ratner is quite correct that we made the enormously simplifying assumption of a unitary decision-maker, the “president,” who need only consult his or her utility function, and whose analysis is rational and unselfish but completely proAmerican. Like everyone, we appreciate that the political realities are far more complex than that, but we believe that our deliberate simplification has the advantage of focusing attention on the simple question of whether the five wars (or military actions) under consideration led to good or bad economic outcomes for the United States as a whole. Clearly, one could address a more realistic decision-theoretic framework in which multiple stakeholders (Congress, generals, intelligence analysts, Halliburton, and many others) negotiate or coalesce or diverge in reaching a military decision, and that would surely lead to fascinating work in sociology and political science. But such modeling was not our intent. And we appreciate Dr. Ratner’s recognition that our primary goal was the cost-benefit analysis. Our emphasis on “the U.S.-centric utility function” bothered Dr. Ratner, and we readily acknowledge that it makes us morally uncomfortable too. We would prefer to live in a world in which the United States is not indifferent to the suffering of others and where altruism is part of the calculus of leadership. And we also think that considerations of decency are usually given some weight in the corridors of power. However, we also believe that a callous calculation of the bottom line is a necessary component of military and other policy decisions. Absent that starting point, there seems to be no principled basis for prioritizing cases and causes. Dr. Ratner would prefer to see “a sensitivity analysis, with an alternative, semi-altruistic utility function.” We think that would be interesting and useful, and effective altruism is always important. But (as Dr. Ratner points out later), our article is already heavily freighted with assumptions that have varying degrees of plausibility. Trying to monetize the lives of non-American
{"title":"Response to “Discretionary Wars, Cost-Benefit Analysis, and the Rashomon Effect”","authors":"David L. Banks","doi":"10.1080/2330443x.2019.1688741","DOIUrl":"https://doi.org/10.1080/2330443x.2019.1688741","url":null,"abstract":"Dr. Jonathan Ratner’s discussion is amazing and a valuable commentary (and sometimes a corrective) upon the work in our article. We are grateful for his thoughtful examination and testing of the assumptions and methodology we have used. His contribution goes far beyond a typical discussion and is an article in its own right, or at the very least a provocative essay. He makes many important points and builds out our reasoning and expands its scope in numerous ways. This response attempts to briefly address some of the key points and suggestions that he makes. Dr. Ratner is quite correct that we made the enormously simplifying assumption of a unitary decision-maker, the “president,” who need only consult his or her utility function, and whose analysis is rational and unselfish but completely proAmerican. Like everyone, we appreciate that the political realities are far more complex than that, but we believe that our deliberate simplification has the advantage of focusing attention on the simple question of whether the five wars (or military actions) under consideration led to good or bad economic outcomes for the United States as a whole. Clearly, one could address a more realistic decision-theoretic framework in which multiple stakeholders (Congress, generals, intelligence analysts, Halliburton, and many others) negotiate or coalesce or diverge in reaching a military decision, and that would surely lead to fascinating work in sociology and political science. But such modeling was not our intent. And we appreciate Dr. Ratner’s recognition that our primary goal was the cost-benefit analysis. Our emphasis on “the U.S.-centric utility function” bothered Dr. Ratner, and we readily acknowledge that it makes us morally uncomfortable too. We would prefer to live in a world in which the United States is not indifferent to the suffering of others and where altruism is part of the calculus of leadership. And we also think that considerations of decency are usually given some weight in the corridors of power. However, we also believe that a callous calculation of the bottom line is a necessary component of military and other policy decisions. Absent that starting point, there seems to be no principled basis for prioritizing cases and causes. Dr. Ratner would prefer to see “a sensitivity analysis, with an alternative, semi-altruistic utility function.” We think that would be interesting and useful, and effective altruism is always important. But (as Dr. Ratner points out later), our article is already heavily freighted with assumptions that have varying degrees of plausibility. Trying to monetize the lives of non-American","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443x.2019.1688741","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42677444","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 : 2019-01-01DOI: 10.1080/2330443X.2019.1619427
Wendy K. Tam Cho, Simon Rubinstein-Salzedo
We thank Chikina, Frieze, and Pegden for their reply to our article. We offer just a short clarification rejoinder. In particular, we would like to be clear that we are not challenging the CFP test as a partisan gerrymandering test. We also do not “cast doubt” on the CFP paper. We have clearly stated that “we take no issues with the mathematics behind the CFP theorem or its proof.” In addition, we do not “prefer” one partisan gerrymandering test over another or advocate a single test. We firmly believe that there is plenty of room for multiple partisan gerrymandering tests. In this space, one test need not be “worse” than another. At the same time, it is indisputable that whether the CFP test would constitute a legal test for partisan gerrymandering is a legal question for the courts to decide. Legal questions cannot be decided by mathematicians. Mathematicians may make proposals, but judges decide whether to accept those proposals. Our point is simply that judges must clearly understand the mathematical concepts (even if not the mathematical details) in order to make a reasoned judgment. However, when the science is unclear, we have only miscommunication, from which no one benefits.
{"title":"Rejoinder to “Understanding our Markov Chain Significance Test”","authors":"Wendy K. Tam Cho, Simon Rubinstein-Salzedo","doi":"10.1080/2330443X.2019.1619427","DOIUrl":"https://doi.org/10.1080/2330443X.2019.1619427","url":null,"abstract":"We thank Chikina, Frieze, and Pegden for their reply to our article. We offer just a short clarification rejoinder. In particular, we would like to be clear that we are not challenging the CFP test as a partisan gerrymandering test. We also do not “cast doubt” on the CFP paper. We have clearly stated that “we take no issues with the mathematics behind the CFP theorem or its proof.” In addition, we do not “prefer” one partisan gerrymandering test over another or advocate a single test. We firmly believe that there is plenty of room for multiple partisan gerrymandering tests. In this space, one test need not be “worse” than another. At the same time, it is indisputable that whether the CFP test would constitute a legal test for partisan gerrymandering is a legal question for the courts to decide. Legal questions cannot be decided by mathematicians. Mathematicians may make proposals, but judges decide whether to accept those proposals. Our point is simply that judges must clearly understand the mathematical concepts (even if not the mathematical details) in order to make a reasoned judgment. However, when the science is unclear, we have only miscommunication, from which no one benefits.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2019.1619427","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48280021","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 : 2019-01-01DOI: 10.1080/2330443x.2019.1666761
P. Harmon, Sarah M McKnight, L. Hildreth, I. Godwin, M. Greenwood
Abstract The Carnegie Classification of Institutions of Higher Education is a commonly used framework for institutional classification that classifies doctoral-granting schools into three groups based on research productivity. Despite its wide use, the Carnegie methodology involves several shortcomings, including a lack of thorough documentation, subjectively placed thresholds between institutions, and a methodology that is not completely reproducible. We describe the methodology of the 2015 and 2018 updates to the classification and propose an alternative method of classification using the same data that relies on structural equation modeling (SEM) of latent factors rather than principal component-based indices of productivity. In contrast to the Carnegie methodology, we use SEM to obtain a single factor score for each school based on latent metrics of research productivity. Classifications are then made using a univariate model-based clustering algorithm as opposed to subjective thresholding, as is done in the Carnegie methodology. Finally, we present a Shiny web application that demonstrates sensitivity of both the Carnegie Classification and SEM-based classification of a selected university and generates a table of peer institutions in line with the stated goals of the Carnegie Classification.
{"title":"An Alternative to the Carnegie Classifications: Identifying Similar Doctoral Institutions With Structural Equation Models and Clustering","authors":"P. Harmon, Sarah M McKnight, L. Hildreth, I. Godwin, M. Greenwood","doi":"10.1080/2330443x.2019.1666761","DOIUrl":"https://doi.org/10.1080/2330443x.2019.1666761","url":null,"abstract":"Abstract The Carnegie Classification of Institutions of Higher Education is a commonly used framework for institutional classification that classifies doctoral-granting schools into three groups based on research productivity. Despite its wide use, the Carnegie methodology involves several shortcomings, including a lack of thorough documentation, subjectively placed thresholds between institutions, and a methodology that is not completely reproducible. We describe the methodology of the 2015 and 2018 updates to the classification and propose an alternative method of classification using the same data that relies on structural equation modeling (SEM) of latent factors rather than principal component-based indices of productivity. In contrast to the Carnegie methodology, we use SEM to obtain a single factor score for each school based on latent metrics of research productivity. Classifications are then made using a univariate model-based clustering algorithm as opposed to subjective thresholding, as is done in the Carnegie methodology. Finally, we present a Shiny web application that demonstrates sensitivity of both the Carnegie Classification and SEM-based classification of a selected university and generates a table of peer institutions in line with the stated goals of the Carnegie Classification.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443x.2019.1666761","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46951949","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 : 2019-01-01DOI: 10.1080/2330443X.2018.1564639
S. Millard
Abstract The United States Environmental Protection Agency (USEPA) is responsible for overseeing the cleanup of sites that fall within the jurisdiction of the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA; also known as “Superfund”). This process almost always involves a remedial investigation/feasibility (RI/FS) study, including deriving upper confidence, prediction, and/or tolerance limits based on concentrations from a designated “background” area which are subsequently used to determine whether a remediated site has achieved compliance. Past USEPA guidance states outlying observations in the background data should not be removed based solely on statistical tests, but rather on some scientific or quality assurance basis. However, recent USEPA guidance states “extreme” outliers, based on tests that assume a normal (Gaussian) distribution, should always be removed from background data, and because “extreme” is not defined, USEPA has interpreted this to mean all outliers identified by a test should be removed. This article discusses problems with current USEPA guidance and how it contradicts past guidance, and illustrates USEPA’s current policy via a case study of the Portland, Oregon Harbor Superfund site. Additional materials, including R code, data, and documentation of correspondence are available in the online supplement.
{"title":"EPA is Mandating the Normal Distribution","authors":"S. Millard","doi":"10.1080/2330443X.2018.1564639","DOIUrl":"https://doi.org/10.1080/2330443X.2018.1564639","url":null,"abstract":"Abstract The United States Environmental Protection Agency (USEPA) is responsible for overseeing the cleanup of sites that fall within the jurisdiction of the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA; also known as “Superfund”). This process almost always involves a remedial investigation/feasibility (RI/FS) study, including deriving upper confidence, prediction, and/or tolerance limits based on concentrations from a designated “background” area which are subsequently used to determine whether a remediated site has achieved compliance. Past USEPA guidance states outlying observations in the background data should not be removed based solely on statistical tests, but rather on some scientific or quality assurance basis. However, recent USEPA guidance states “extreme” outliers, based on tests that assume a normal (Gaussian) distribution, should always be removed from background data, and because “extreme” is not defined, USEPA has interpreted this to mean all outliers identified by a test should be removed. This article discusses problems with current USEPA guidance and how it contradicts past guidance, and illustrates USEPA’s current policy via a case study of the Portland, Oregon Harbor Superfund site. Additional materials, including R code, data, and documentation of correspondence are available in the online supplement.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2018.1564639","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47615775","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 : 2019-01-01DOI: 10.1080/2330443X.2019.1637802
Michael D. Collins
Abstract Many sightings of the Ivory-billed Woodpecker (Campephilus principalis) have been reported during the past several decades, but nobody has managed to obtain the clear photo that is regarded as the standard form of evidence for documenting birds. Despite reports of sightings by teams of ornithologists working independently in Arkansas and Florida, doubts cast on the persistence of this iconic species have impeded the establishment of a meaningful conservation program. An analysis of the expected waiting time for obtaining a photo provides insights into why the policy of insisting upon ideal evidence has failed for this species. Concepts in statistics and probability are used to analyze video footage that was obtained during encounters with birds that were identified in the field as Ivory-billed Woodpeckers. One of the videos shows a series of events that are consistent with that species and are believed to be inconsistent with every other species of the region. Another video shows a large bird in flight with the distinctive wing motion of a large woodpecker. Only two large woodpeckers occur in the region, and the flap rate is about ten standard deviations greater than the mean flap rate of the Pileated Woodpecker (Dryocopus pileatus). Supplemental materials for this article are available online.
{"title":"Statistics, Probability, and a Failed Conservation Policy","authors":"Michael D. Collins","doi":"10.1080/2330443X.2019.1637802","DOIUrl":"https://doi.org/10.1080/2330443X.2019.1637802","url":null,"abstract":"Abstract Many sightings of the Ivory-billed Woodpecker (Campephilus principalis) have been reported during the past several decades, but nobody has managed to obtain the clear photo that is regarded as the standard form of evidence for documenting birds. Despite reports of sightings by teams of ornithologists working independently in Arkansas and Florida, doubts cast on the persistence of this iconic species have impeded the establishment of a meaningful conservation program. An analysis of the expected waiting time for obtaining a photo provides insights into why the policy of insisting upon ideal evidence has failed for this species. Concepts in statistics and probability are used to analyze video footage that was obtained during encounters with birds that were identified in the field as Ivory-billed Woodpeckers. One of the videos shows a series of events that are consistent with that species and are believed to be inconsistent with every other species of the region. Another video shows a large bird in flight with the distinctive wing motion of a large woodpecker. Only two large woodpeckers occur in the region, and the flap rate is about ten standard deviations greater than the mean flap rate of the Pileated Woodpecker (Dryocopus pileatus). Supplemental materials for this article are available online.","PeriodicalId":43397,"journal":{"name":"Statistics and Public Policy","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/2330443X.2019.1637802","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60065970","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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