No full-text available
To read the full-text of this research,
you can request a copy directly from the author.
W.S. Gosset and Some Neglected Concepts in Experimental Statistics: Guinnessometrics II
Abstract
Student's exacting theory of errors, both random and real, marked a significant advance over ambiguous reports of plant life and fermentation asserted by chemists from Priestley and Lavoisier down to Pasteur and Johannsen, working at the Carlsberg Laboratory. One reason seems to be that William Sealy Gosset (1876–1937) aka “Student” – he of Student's t-table and test of statistical significance – rejected artificial rules about sample size, experimental design, and the level of significance, and took instead an economic approach to the logic of decisions made under uncertainty. In his job as Apprentice Brewer, Head Experimental Brewer, and finally Head Brewer of Guinness, Student produced small samples of experimental barley, malt, and hops, seeking guidance for industrial quality control and maximum expected profit at the large scale brewery. In the process Student invented or inspired half of modern statistics. This article draws on original archival evidence, shedding light on several core yet neglected aspects of Student's methods, that is, Guinnessometrics, not discussed by Ronald A. Fisher (1890–1962). The focus is on Student's small sample, economic approach to real error minimization, particularly in field and laboratory experiments he conducted on barley and malt, 1904 to 1937. Balanced designs of experiments, he found, are more efficient than random and have higher power to detect large and real treatment differences in a series of repeated and independent experiments. Student's world-class achievement poses a challenge to every science. Should statistical methods – such as the choice of sample size, experimental design, and level of significance – follow the purpose of the experiment, rather than the other way around? (JEL classification codes: C10, C90, C93, L66)
... 14 Though Levitt and List mention Student (1923) and Heckman and Vytlacil (2007) their article does not mention that these and the other studies are evidence against randomization and in favor of balance. For discussion of Fisher's methods, and how they differ from the methods of Student, Egon Pearson, Harold Jeffreys, and other experimental economists -the decisiontheorists and Bayesian experimentalists not mentioned by Levitt and List -see Zellner (2004), Ziliak (2008Ziliak ( , 2010Ziliak ( , 2011b, Ziliak and McCloskey (2008, Chapters 1, 17-23), McCloskey and Ziliak (2009), and Harrison (2011). economic stakes and confounding are large, overturning the so-called principle of randomization. ...
... 243-244). 16 Gosset was a self-trained innovator of experimental design and analysis, skills he learned on the job at Guinness's brewery, where he worked for his entire adult life: Apprentice Brewer (1899-1906Head Experimental Brewer (1907-1935; Head of Statistics Department (1922)(1923)(1924)(1925)(1926)(1927)(1928)(1929)(1930)(1931)(1932)(1933)(1934)(1935); Head Brewer, Park Royal location (1935)(1936)(1937); Head Brewer, Dublin and Park Royal (1937) (see Ziliak, 2011aZiliak, , 2011bZiliak, , 2008. 17 Student's legacy at the frontier is extensive, though not always acknowledged: Pearson (1938Pearson ( , 1939Pearson ( , 1990, Neyman et al. (1935), Neyman (1938), Jeffreys (1939Jeffreys ( [1961), Savage (1954Savage ( , 1976, Deming (1978), Kruskal (1980), Zellner and Rossi (1986), Heckman (1991), Press (2003), and Heckman and Vytlacil (2007), to name a few. ...
... Balancing is necessary when unobserved attitudes about schooling, gender, and race contribute to selection bias in the outcomes of social programs, as they often do (Heckman and Vytlacil, 2007;Bruhn and McKenzie, 2009). Balancing means creation of symmetry in all of the important sources of fluctuation, chance and systematic, random and real; thus balancing is more, not less, valid than complete randomization (Ziliak, 2011b). As Student observed, in short, the need to balance is present any time there is a secular or temporal correlation of the dependent variable (such as crop yield) with one or more systematic, non-random independent variables (such as soil fertility) which cannot be artificially randomized. ...
Over the past decade randomized field experiments have gained prominence in the toolkit of empirical economics and policy making. In an article titled "Field Experiments in Economics: The Past, the Present, and the Future," Levitt and List (2009) make three main claims about the history, philosophy, and future of field experiments in economics. (1) They claim that field experiments in economics began in the 1920s and 1930s in agricultural work by Neyman and Fisher. (2) They claim that artificial randomization is essential for good experimental design because, they claim, randomization is the only valid justification for Student's test of significance. (3) They claim that decision-making in private sector firms will be advanced by partnering with economists doing randomized experiments. Several areas of research have been influenced by the article despite the absence of historical and methodological review. This paper seeks to fill that gap in the literature. The power and efficiency of balanced over random designs — discovered by William S. Gosset aka Student, and confirmed by Pearson, Neyman, Jeffreys, and others adopting a balanced, decision-theoretic and/or Bayesian approach to experiments — is not mentioned in the Levitt and List article. Neglect of Student is regrettable. A body of evidence descending from Student (1911) and extending to Heckman and Vytlacil (2007) suggests that artificial randomization is neither necessary nor sufficient for improving efficiency, identifying causal relationships, and discovering economically significant differences. Three easy ways to improve field experiments are proposed and briefly illustrated.
... This methodology begins with quantifying risk and then focuses on prevention, detection, and remediation. Consequently, Lean Six Sigma is used to mitigate risks in three ways, including preventing incidents from happening, detecting incidents as early as possible, and minimizing the impact of incidents that occur [21]. By focusing on these three elements, organizations can minimize the damage caused by cyberattacks and improve their resilience to future threats. ...
This study aims to describe and assess the impact of critical infrastructure (CI) cybersecurity issues on the sustainable development of smart cities. This study highlights the integration of PayTech systems into the broader CI landscape, highlighting their impact on maintaining economic stability and ensuring the smooth operation of city services. Key companies within smart regions, particularly those operating in the payment industries, are essential to maintaining the functionality of critical services. These companies facilitate the processing of services provided to citizens, enabling access to vital municipal services. As key players in the PayTech and online e-commerce sectors, they form a crucial part of modern critical infrastructure, operating within an ever-evolving digital environment. This study examines the recovery processes employed after cyberattacks, focusing on the differing perspectives of internal and external professionals. It identifies significant differences in the perceptions of recovery strategies among internal stakeholders, such as investor relations (IR) teams, reputation management (RM) experts, and Chief Information Security Officers (CISOs), who represent critical infrastructure companies. Additionally, it explores the roles of external auditors, who provide impartial emergency support and perform specialized recovery tasks. Importantly, this study underscores the current attitudes toward future information security strategies and their influence on the financial recovery and reputation of reliable companies following cyber incidents. This research contributes to the existing knowledge by shedding light on the perspectives of both a company’s internal and external specialists involved in the recovery process and cyber resilience strategies in critical infrastructure sectors.
... Calculation of the mean and random error based on Student's test -hypothesis test statistic. This method developed by statistician William Sealy Gosset was most commonly applied when the test statistic would follow a normal distribution of the value [12]. Student's t-test: ...
Companies operating in the PayTech and online e-commerce sectors play a crucial role in critical infrastructure, functioning within the dynamic digital landscape. This study focuses on the recovery process after cyber-attacks and examines the contrasting perspectives of internal and external professionals. The research reveals notable differences in the perceptions of recovery strategies between internal stakeholders such as investor relations, reputation management, and Chief Information Security Officers, representing companies belonging to critical infrastructure and external auditors, who provide just and emergency support and perform specific tasks. Importantly, the study underscores the current attitudes towards future information security strategies and their influence on the financial recovery and reputation of reliable companies following cyber incidents. This research contributes to the existing knowledge by shedding light on the perspectives of both a company's internal and external specialists involved in the recovery process and cyber resilience strategies in critical infrastructure sectors.
... In lieu of such high sample numbers given current SOC sampling limitations, we account for confounding variables and variation in SOC stocks both within and among fields through study and sampling design. Our approach follows 'small sample' causal work that was advanced >100 years ago in applied agricultural science to quantify management effects and guide decision making (Student, 1923;Ziliak, 2011). This historical work advocated for research designs that controlled for confounding factors and collected data at the grain of working fields, arrayed across farms representative of the heterogeneity in conditions and managements in the agricultural region. ...
... In lieu of such high sample numbers given current SOC sampling limitations, we account for confounding variables and variation in SOC stocks both within and among fields through study and sampling design. Our approach follows 'small sample' causal work that was advanced >100 years ago in applied agricultural science to quantify management effects and guide decision making (Student, 1923;Ziliak, 2011). This historical work advocated for research designs that controlled for confounding factors and collected data at the grain of working fields, arrayed across farms representative of the heterogeneity in conditions and managements in the agricultural region. ...
There is a deep and well-regarded tradition in applied economic research and in other social sciences to assign causality to correlation analysis and statistical significance. I critique the application of correlation analysis, unsubstantiated with empirical backing of prior assumptions, as the core analytical measure for causation. Moreover, this paper critiques the past and current focus on statistical significance as the core indicator of substantive or analytical significance, especially when paired with correlation analysis. I attempt to explain the persistence of the misplaced use of these statistical techniques in the applied literature. I also propose a positive analytical frame wherein correlation analysis and tests of statistical significance, as part of a large analytical toolbox, can make a positive contribution to the analytical literature.
Econometricians have been claiming proudly since World War II that significance testing is the empirical side of economics. In fact today most young economists think that the word "empirical" simply means "collect enough data to do a significance test". Tjalling Koopmans's influential book of 1957, Three Essays on the State of Economic Science, solidified the claim. A century of evidence after Student's t-test points strongly to the opposite conclusion. Against conventional econometrics we argue that statistical significance is neither necessary nor sufficient for proving commercial, human, or scientific importance. A recent comment by Thomas Mayer, though in parts insightful, does nothing to alter conclusions about the logic and evidence which we and others have assembled against significance testing. Let's bury it, and get on to empirical work that actually changes minds.
This paper discusses the statistical reasoning in judicial contexts. Courts in modern world encounter complex cases that require the proof by statistical evidences, and the interpretations of such evidences need a sound statistical reasoning. Judge Oliver Holmes opined in 1897 that the man of the future is the man of statistics and the master of economics, and that future time has entered the judicial scene in the 20 th century. Starting from allowing statistical evidence into court by classifying it as one of the hearsay rule exceptions, the courts formulate more and more principles on guiding how lawyers exercise their statistical reasoning. In the recent case of Matrixx (2011), the U.S. Supreme Court took a long discussion on the proper statistical reasoning approach on interpreting test of significance result when p-value less than 0.05. The authors submit that with the advance in technologies, the use of statistical evidences as means of judicial proof will become more and more common. Statistician seems to become the required expert witness in cases related to discrimination, antitrust, and pharmaceutical torts, statistical reasoning becomes a new way to prove a complicated case by lawyers. It is no wonder that when one search the phrase "conference on forensic statistics", the google search will list about 3,620 results.
Bayesian rationality is the paradigm of rational behavior in neoclassical economics. A rational agent in an economic model is one who maximizes her subjective expected utility and consistently revises her beliefs according to Bayes’s rule. The paper raises the question of how, when and why this characterization of rationality came to be endorsed by mainstream economists. Though no definitive answer is provided, it is argued that the question is of great historiographic importance. The story begins with Abraham Wald’s behaviorist approach to statistics and culminates with Leonard J. Savage’s elaboration of subjective expected utility theory in his 1954 classic The Foundations of Statistics. It is the latter’s acknowledged fiasco to achieve its planned goal, the reinterpretation of traditional inferential techniques along subjectivist and behaviorist lines, which raises the puzzle of how a failed project in statistics could turn into such a tremendous hit in economics. A couple of tentative answers are also offered, involving the role of the consistency requirement in neoclassical analysis and the impact of the postwar transformation of US business schools.
We present new evidence on the randomization methods used in existing experiments, and new simulations comparing these methods. We find that many papers do not describe the randomization in detail, implying that better reporting is needed. Our simulations suggest that in samples of 300 or more, the different methods perform similarly. However, for very persistent outcome variables, and in smaller samples, pair-wise matching and stratification perform best and appear to dominate the rerandomization methods commonly used in practice. The simulations also point to specific recommendations for which variables to balance on, and for which controls to include in the ex post analysis. (JEL C83, C93, O12)
We conducted a natural field experiment to explore the effect of price changes on charitable contributions. To operationalize our tests, we examine whether an offer to match contributions to a non-profit organization changes the likelihood and amount that an individual donates. Direct mail solicitations were sent to over 50,000 prior donors. We find that the match offer increases both the revenue per solicitation and the probability that an individual donates. While comparisons of the match treatments and the control group consistently reveal this pattern, larger match ratios (i.e., 3:1 and 2:1) relative to smaller match ratios (1:1) had no additional impact. The results have clear implications for practitioners in the design of fundraising campaigns and provide avenues for future empirical and theoretical work on charitable giving. Further, the data provide an interesting test of important methods used in cost-benefit analysis.
The chapters in this book demonstrate that 'beeronomics' covers a vast set of economic issues: economic history, development, demand and supply, geography, trade, investment, technology, health and nutrition, quantity and quality, industrial organization, competition, science and innovation, taxation, regulation, political economy, etc. This concluding chapter summarizes some of the key insights of this book, organized in five sections: a historical perspective on the product 'beer', consumption, industrial organization, trade and international expansion, and government regulation. The final section discusses a set of unresolved issues and a future research agenda in the field of beeronomics.
Antoine Lavoisier, the author of the "chemical revolution," also did much to estabish the foundations for the fields of organic chemistry and biochemistry. Here, Frederic Lawrence Holmes gives us an intimate portrait of Lavoisier's investigations, ranging over twenty years, from 1773 to 1792, on respiration, fermentation, and plant and animal matter. These studies, Holmes finds, were not simply belated applications of Lavoisier's established chemical theories, but intimately bound from the beginning to his more widely known research on combustion and calcination.
Letters or extracts from letters which passed between W. S. Gosset, R. A. Fisher and Karl Pearson during the years 1912-20 are reproduced. They throw light on the start of Fisher's statistical career. In the notes accompanying the correspondence, attention is drawn to the problems in estimation and significance testing which were brought to the front at that date by the unusual character of the sampling distribution of the correlation coefficient. An early disagreement between Pearson and Fisher on estimation through minimizing χ2 and maximizing likelihood is covered.
The prime object of this book is to put into the hands of research workers, and especially of biologists, the means of applying statistical tests accurately to numerical data accumulated in their own laboratories or available in the literature.
Randomised control trials have become popular tools in development economics. The key idea is to exploit deliberate or naturally occurring randomisation of treatments in order to make causal inferences about "what works" to promote some development objective. The expression"what works" is crucial: the emphasis is on evidence-based conclusions that will have immediate policy use. No room for good intentions, wishful thinking, ideological biases, Washington Consensus, cost-benefit calculations or even parametric stochastic assumptions. A valuable byproduct has been the identification of questions that other methods might answer, or that subsequent randomised evaluations might address. An unattractive byproduct has been the dumbing down of econometric practice, the omission of any cost-benefit analytics and an arrogance towards other methodologies. Fortunately, the latter are gratuitous, and the former point towards important complementarities in methods to help address knotty, substantive issues in development economics. Copyright 2011 , Oxford University Press.
In an article titled "Field Experiments in Economics: The Past, the Present, and the Future," Levitt and List (2009) make three important claims about the history, philosophy, and future of field experiments in economics. They claim that field experiments in economics began in the 1920s and 1930s, in agricultural work by Neyman and Fisher. Second, they claim that artificial randomization is the sine qua non of good experimental design; they claim that randomization is the only valid justification for use of Student‘s test of significance. Finally, they claim that the theory of the firm will be advanced by economists doing randomized controlled trials (RCTs) for private sector firms. Several areas of economics, for example the development economics of Banerjee and Duflo, have been influenced by the article, despite the absence of historical and methodological review. This comment seeks to fill that gap in the literature. Student has, it is found, priority over Fisher and Neyman; he compared balanced and random designs in the field—on crops from barley to timber—from 1905 to 1937. The power and efficiency of balanced over random designs - discovered by Student and confirmed by Pearson, Neyman, Jeffreys, and others adopting a decision-theoretic and/or Bayesian approach - is not mentioned by Levitt and List. Neglect of Student is especially regrettable, for he showed in his job as Head Brewer of Guinness that artificial randomization is neither necessary nor sufficient for improving efficiency, identifying causal relationships, or discovering economically significant differences. One way forward is to take a step backwards, from Fisher to Student.
The present position of the art of field experimentation is one of rather special interest. For more than fifteen years the attention of agriculturalists has been turned to the errors of field experiments. During this period, experiments of the uniformity trial type have demonstrated the magnitude and ubiquity of that class of error which cannot be ascribed to carelessness in measuring the land or weighing the produce, and which is consequently described as due to “soil heterogeneity”; much ingenuity has been expended in devising plans for the proper arrangement of the plots; and not without result, for there can be little doubt that the standard of accuracy has been materially, though very irregularly, raised. What makes the present position interesting is that it is now possible to demonstrate (a) that the actual position of the problem is very much more intricate than was till recently imagined, but that realising this (b) the problem itself becomes much more definite and (c) its solution correspondingly more rigorous.
"Billions of government dollars, and thousands of charitable organizations and NGOs, are dedicated to helping the world's poor. But much of the work they do is based on assumptions that are untested generalizations at best, flat out harmful misperceptions at worst. Banerjee and Duflo have pioneered the use of randomized control trials in development economics. Work based on these principles, supervised by the Poverty Action Lab at MIT, is being carried out in dozens of countries. Their work transforms certain presumptions: that microfinance is a cure-all, that schooling equals learning, that poverty at the level of 99 cents a day is just a more extreme version of the experience any of us have when our income falls uncomfortably low. Throughout, the authors emphasize that life for the poor is simply not like life for everyone else: it is a much more perilous adventure, denied many of the cushions and advantages that are routinely provided to the more affluent"--
The magnitude of experimental error attaching to one or more field plots is a question of extreme importance in Agricultural Science, because upon its proper recognition depends the degree of confidence which may be attached to the results obtained in field work. A very cursory examination of the results of any set of field trials will serve to show that a pair of plots similarly treated may be expected to yield considerably different results, even when the soil appears to be uniform and the conditions under which the experiment is conducted are carefully designed to reduce errors in weighing and measurement.
“Matrixx's argument rests on the premise that statistical significance is the only reliable indication of causation. This premise is flawed.” So ruled the US Supreme Court this year in a judgment of huge significance. As a drug company learns that it must disclose even statistically insignificant side effects, Stephen T. Ziliak congratulates the judges, and two giants of statistics fight on from their graves.
J. Inst. Brew. 115(1), 3–29, 2009 Horace Brown spent fifty years conducting brewing research in Burton-on-Trent, Dublin and London. His contributions were remarkable and his focus was to solve practical brewing prob-lems by employing and developing fundamental scientific prin-ciples. He studied all aspects of the brewing process including raw materials, wort preparation, fermentation, yeast and beer stability. As a number of previous presenters of the Horace Brown Lecture have discussed Brown's achievements in detail, the focus of this paper is a review of the brewing research that has been conducted by the author and his colleagues during the past forty years. Similar to Horace Brown, fundamental research has been employed to solve brewing problems. Research studies that are discussed in this review paper include reasons for pre-mature flocculation of ale strains resulting in wort underattenua-tion including mechanisms of co-flocculation and pure strain flocculation, storage procedures for yeast cultures prior to prop-agation, studies on the genetic manipulation of brewer's yeast strains with an emphasis on the FLO1 gene, spheroplast fusion and the respiratory deficient (petite) mutation, the uptake and metabolism of wort sugars and amino acids, the influence of wort density on fermentation characteristics and beer flavour and stability, and finally, the contribution that high gravity brewing has on brewing capacity, fermentation efficiency and beer quality and stability.
The articles published by the Annals of Eugenics (1925–1954) have been made available online as an historical archive intended for scholarly use. The work of eugenicists was often pervaded by prejudice against racial, ethnic and disabled groups. The online publication of this material for scholarly research purposes is not an endorsement of those views nor a promotion of eugenics in any way.
Based on two lectures presented as part of The Stone Lectures in Economics series, Arnold Zellner describes the structural econometric time series analysis (SEMTSA) approach to statistical and econometric modeling. Developed by Zellner and Franz Palm, the SEMTSA approach produces an understanding of the relationship of univariate and multivariate time series forecasting models and dynamic, time series structural econometric models. As scientists and decision-makers in industry and government world-wide adopt the Bayesian approach to scientific inference, decision-making and forecasting, Zellner offers an in-depth analysis and appreciation of this important paradigm shift. Finally Zellner discusses the alternative approaches to model building and looks at how the use and development of the SEMTSA approach has led to the production of a Marshallian Macroeconomic Model that will prove valuable to many. Written by one of the foremost practitioners of econometrics, this book will have wide academic and professional appeal.
Agricultural economics that has played a prime role in economics for a long time is losing its importance more recently. The future of field experimental methods can be importantly engineered by agricultural and resource economists. The field experimenter does not exert the same degree of control over real markets as the scientist does in the lab. Agricultural economists have a comparative advantage in several factors instrumental in conducting cutting-edge field experimental research. Scholars working on agricultural issues once held a prominent role in conceptualizing key empirical approaches that impacted the economics profession more generally. It is time theorists combine with extension specialists to lend insights into both normative and positive issues of the day since the defeat of the Farm Management Movement. Much requires to be done whether field experiments revolve around exploring optimal incentive schemes of farm laborers in Europe, measuring demand elasticities for Bt cotton in the South.
This chapter relates the literature on the econometric evaluation of social programs to the literature in statistics on “causal inference”. In it, we develop a general evaluation framework that addresses well-posed economic questions and analyzes agent choice rules and subjective evaluations of outcomes as well as the standard objective evaluations of outcomes. The framework recognizes uncertainty faced by agents and ex ante and ex post evaluations of programs. It also considers distributions of treatment effects. These features are absent from the statistical literature on causal inference. A prototypical model of agent choice and outcomes is used to illustrate the main ideas.We formally develop models for counterfactuals and causality that build on Cowles Commission econometrics. These models anticipate and extend the literature on causal inference in statistics. The distinction between fixing and conditioning that has recently entered the statistical literature was first developed by Cowles economists. Models of simultaneous causality were also developed by the Cowles group, as were notions of invariance to policy interventions. These basic notions are updated to nonlinear and nonparametric frameworks for policy evaluation more general than anything in the current statistical literature on “causal inference”. A formal discussion of identification is presented and applied to clearly formulated choice models used to evaluate social programs.
This paper is a practical guide (a toolkit) for researchers, students and practitioners wishing to introduce randomization as part of a research design in the field. It first covers the rationale for the use of randomization, as a solution to selection bias and a partial solution to publication biases. Second, it discusses various ways in which randomization can be practically introduced in a field settings. Third, it discusses designs issues such as sample size requirements, stratification, level of randomization and data collection methods. Fourth, it discusses how to analyze data from randomized evaluations when there are departures from the basic framework. It reviews in particular how to handle imperfect compliance and externalities. Finally, it discusses some of the issues involved in drawing general conclusions from randomized evaluations, including the necessary use of theory as a guide when designing evaluations and interpreting results.
âMcCloskey and Ziliak have been pushing this very elementary, very correct, very important argument through several articles over several years and for reasons I cannot fathom it is still resisted. If it takes a book to get it across, I hope this book will do it. It ought to.â âThomas Schelling, Distinguished University Professor, School of Public Policy, University of Maryland, and 2005 Nobel Prize Laureate in Economics âWith humor, insight, piercing logic and a nod to history, Ziliak and McCloskey show how economistsâand other scientistsâsuffer from a mass delusion about statistical analysis. The quest for statistical significance that pervades science today is a deeply flawed substitute for thoughtful analysis. . . . Yet few participants in the scientific bureaucracy have been willing to admit what Ziliak and McCloskey make clear: the emperor has no clothes.â âKenneth Rothman, Professor of Epidemiology, Boston University School of Health The Cult of Statistical Significance shows, field by field, how âstatistical significance,â a technique that dominates many sciences, has been a huge mistake. The authors find that researchers in a broad spectrum of fields, from agronomy to zoology, employ âtestingâ that doesnât test and âestimatingâ that doesnât estimate. The facts will startle the outside reader: how could a group of brilliant scientists wander so far from scientific magnitudes? This study will encourage scientists who want to know how to get the statistical sciences back on track and fulfill their quantitative promise. The book shows for the first time how wide the disaster is, and how bad for science, and it traces the problem to its historical, sociological, and philosophical roots. Stephen T. Ziliak is the author or editor of many articles and two books. He currently lives in Chicago, where he is Professor of Economics at Roosevelt University. Deirdre N. McCloskey, Distinguished Professor of Economics, History, English, and Communication at the University of Illinois at Chicago, is the author of twenty books and three hundred scholarly articles. She has held Guggenheim and National Humanities Fellowships. She is best known for How to Be Human* Though an Economist (University of Michigan Press, 2000) and her most recent book, The Bourgeois Virtues: Ethics for an Age of Commerce (2006).
Fisher's contributions to statistics are surveyed. His background, skills, temperament, and style of thought and writing are sketched. His mathematical and methodological contributions are outlined. More attention is given to the technical concepts he introduced or emphasized, such as consistency, sufficiency, efficiency, information, and maximum likelihood. Still more attention is given to his conception and concepts of probability and inference, including likelihood, the fiducial argument, and hypothesis testing. Fisher is at once very near to and very far from modern statistical thought generally.
References to the economy are ubiquitous in modern life, and virtually every facet of human activity has capitulated to market mechanisms. In the early modern period, however, there was no common perception of the economy, and discourses on money, trade, and commerce treated economic phenomena as properties of physical nature. Only in the early nineteenth century did economists begin to posit and identify the economy as a distinct object, divorcing it from natural processes and attaching it exclusively to human laws and agency. In The Natural Origins of Economics, Margaret Schabas traces the emergence and transformation of economics in the eighteenth and nineteenth centuries from a natural to a social science. Focusing on the works of several prominent economistsâDavid Hume, Adam Smith, Thomas Malthus, David Ricardo, and John Stuart MillâSchabas examines their conceptual debt to natural science and thus locates the evolution of economic ideas within the history of science. An ambitious study, The Natural Origins of Economics will be of interest to economists, historians, and philosophers alike.
This study presents an overview of modern field experiments and their usage in economics. Our discussion focuses on three distinct periods of field experimentation that have influenced the economics literature. The first might well be thought of as the dawn of "field" experimentation: the work of Neyman and Fisher, who laid the experimental foundation in the 1920s and 1930s by conceptualizing randomization as an instrument to achieve identification via experimentation with agricultural plots. The second, the large-scale social experiments conducted by government agencies in the mid-twentieth century, moved the exploration from plots of land to groups of individuals. More recently, the nature and range of field experiments has expanded, with a diverse set of controlled experiments being completed outside of the typical laboratory environment. With this growth, the number and types of questions that can be explored using field experiments has grown tremendously. After discussing these three distinct phases, we speculate on the future of field experimental methods, a future that we envision including a strong collaborative effort with outside parties, most importantly private entities.
In economics and other sciences, "statistical significance" is by custom, habit, and education a necessary and sufficient condition for proving an empirical result. The canonical routine is to calculate what’s called a t-statistic and then to compare its estimated value against a theoretically expected value of it, which is found in "Student's" t table. A result yielding a t-value greater than or equal to about 2.0 is said to be "statistically significant at the 95 percent level." Alternatively, a regression coefficient is said to be "statistically significantly different from the null, p ? .05." Canonically speaking, if a coefficient clears the 95 percent hurdle, it warrants additional scientific attention. If not, not. The first presentation of "Student's" test of significance came a century ago in 1908, in "The Probable Error of a Mean," published by an anonymous "Student." The author's commercial employer required that his identity be shielded from competitors, but we have known for some decades that the article was written by William Sealy Gosset (1876–1937), whose entire career was spent at Guinness's brewery in Dublin, where Gosset was a master brewer and experimental scientist. Perhaps surprisingly, the ingenious "Student" did not give a hoot for a single finding of "statistical" significance, even at the 95 percent level of significance as established by his own tables. Beginning in 1904, "Student," who was a businessman besides a scientist, took an economic approach to the logic of uncertainty, arguing finally that statistical significance is "nearly valueless" in itself.
Statistical significance as used in economics has weak theoretical justification. In particular it merges statistical and substantive significance. The 182 papers using regression analysis in the American Economic Review in the 1980s were tested against 19 criteria for the accepted use of statistical significance. Most, some three-quarters of the papers, did poorly. Likewise, textbooks in econometrics do not distinguish statistical and economic significance. Statistical significance should not be the focus of empirical economics.
On the Error of Counting Poor Economics: A Radical Rethinking of the Way to Fight Global Poverty Barley: Fifty Years of Observation and Experiment
- A Banerjee
- E Duflo
Student (1907) is Student's first publication, notably to students of beer, " On the Error of Counting [Yeast Cells] with a Haemacytometer ". 38 Gosset (1904), p. 3. References Banerjee, A. and Duflo, E. (2011). Poor Economics: A Radical Rethinking of the Way to Fight Global Poverty. New York: Public Affairs. Beaven, E.S. (1947). Barley: Fifty Years of Observation and Experiment. London: Duckworth. Brown, H.T., (ed.) (1903). Transactions of the Guinness Research Laboratory, Vol. I, Part I.
Gosset and Some Neglected Concepts in Experimental Statistics Dennison
- W S S R Macdonagh
W.S. Gosset and Some Neglected Concepts in Experimental Statistics Dennison, S.R. and MacDonagh, O. (1998). Guinness 1886–1939.
Prohibition at its Worst. New York: The Macmillan Company
- I Fisher
Fisher, I. (1926). Prohibition at its Worst. New York: The Macmillan Company.
The effects of a full-employment policy on economic stability: a formal analysis
- M Friedman
Friedman, M. (1953). The effects of a full-employment policy on economic stability: a formal analysis. In: Friedman, M., Essays in Positive Economics. Chicago: University of Chicago Press, 117–132.
Student The Application of the 'Law of Error' to the Work of the Brewery
- W S Gosset
Gosset, W.S. [see " Student ", below] (1904). The Application of the 'Law of Error' to the Work of the Brewery. Laboratory Report, 8, Arthur Guinness & Son, Ltd., Diageo, Guinness Archives, 3–16 and unnumbered appendix.