Lord's Paradox

picture info	Lord's Paradox In statistics, Lord's paradox raises the issue of when it is appropriate to control for baseline (configuration management), baseline status. In three papers, Frederic M. Lord gave examples when statisticians could reach different conclusions depending on whether they adjust for pre-existing differences.Lord, E. M. (1967). A paradox in the interpretation of group comparisons. Psychological Bulletin, 68, 304–305. Holland & Rubin (1983) use these examples to illustrate how there may be multiple valid descriptive comparisons in the data, but causal conclusions require an underlying (untestable) causal model. Pearl used these examples to illustrate how graphical causal models resolve the issue of when control for baseline status is appropriate. Lord's formulation The most famous formulation of Lord's paradox comes from his 1967 paper: ::“A large university is interested in investigating the effects on the students of the diet provided in the university dining halls and any sex ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
	Baseline (configuration Management) In Configuration Management, a baseline is an agreed description of the attributes of a product, at a point in time, which serves as a basis for defining change. A change is a movement from this baseline state to a next state. The identification of significant changes from the baseline state is the central purpose of baseline identification.CMMI Product Team, "Chpt 7, Maturity Level 2: Managed, Configuration Management, SP 1.3," in ''Capability Maturity Model Integration, Version 1.1 (CMMI-SE/SW/IPPD/SS, V1.1): Staged Representation,'' Carnegie Mellon Software Engineering Institute. Typically, significant states are those that receive a formal approval status, either explicitly or implicitly. An approval status may be attributed to individual items, when a prior definition for that status has been established by project leaders, or signified by mere association to a particular established baseline. Nevertheless, this approval status is usually recognized publicly. A baseline ma ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
	Frederic M Frederic may refer to: Places United States * Frederic, Wisconsin, a village in Polk County * Frederic Township, Michigan, a township in Crawford County ** Frederic, Michigan, an unincorporated community Other uses * Frederic (band), a Japanese rock band * Frederic (given name), a given name (including a list of people and characters with the name) * Hurricane Frederic, a hurricane that hit the U.S. Gulf Coast in 1979 * Trent Frederic, American ice hockey player See also * Frédéric * Frederick (other) * Fredrik * Fryderyk (other) Fryderyk () is a given name, and may refer to: * Fryderyk Chopin (1810–1849), a Polish piano composer * Fryderyk Getkant (1600–1666), a military engineer, artilleryman and cartographer of German origin * Fryderyk Scherfke (1909–1983), an inte ... {{disambiguation, geo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
picture info	Causal Models In the philosophy of science, a causal model (or structural causal model) is a conceptual model that describes the causal mechanisms of a system. Causal models can improve study designs by providing clear rules for deciding which independent variables need to be included/controlled for. They can allow some questions to be answered from existing observational data without the need for an interventional study such as a randomized controlled trial. Some interventional studies are inappropriate for ethical or practical reasons, meaning that without a causal model, some hypotheses cannot be tested. Causal models can help with the question of ''external validity'' (whether results from one study apply to unstudied populations). Causal models can allow data from multiple studies to be merged (in certain circumstances) to answer questions that cannot be answered by any individual data set. Causal models have found applications in signal processing, epidemiology and machine learning. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
picture info	Analysis Of Covariance Analysis of covariance (ANCOVA) is a general linear model which blends ANOVA and regression. ANCOVA evaluates whether the means of a dependent variable (DV) are equal across levels of a categorical independent variable (IV) often called a treatment, while statistically controlling for the effects of other continuous variables that are not of primary interest, known as covariates (CV) or nuisance variables. Mathematically, ANCOVA decomposes the variance in the DV into variance explained by the CV(s), variance explained by the categorical IV, and residual variance. Intuitively, ANCOVA can be thought of as 'adjusting' the DV by the group means of the CV(s). The ANCOVA model assumes a linear relationship between the response (DV) and covariate (CV): y_ = \mu + \tau_i + \Beta(x_ - \overline) + \epsilon_. In this equation, the DV, y_ is the jth observation under the ith categorical group; the CV, x_ is the ''j''th observation of the covariate under the ''i''th group. Variables in th ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
	:Category:Statistical Paradoxes {{Commons Mathematical paradoxes Paradoxes A paradox is a logically self-contradictory statement or a statement that runs contrary to one's expectation. It is a statement that, despite apparently valid reasoning from true premises, leads to a seemingly self-contradictory or a logically u ... ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
picture info	Simpson's Paradox Simpson's paradox is a phenomenon in probability and statistics in which a trend appears in several groups of data but disappears or reverses when the groups are combined. This result is often encountered in social-science and medical-science statistics, and is particularly problematic when frequency data are unduly given causal interpretations.Judea Pearl. ''Causality: Models, Reasoning, and Inference'', Cambridge University Press (2000, 2nd edition 2009). . The paradox can be resolved when confounding variables and causal relations are appropriately addressed in the statistical modeling. Simpson's paradox has been used to illustrate the kind of misleading results that the misuse of statistics can generate. Edward H. Simpson first described this phenomenon in a technical paper in 1951, but the statisticians Karl Pearson (in 1899) and Udny Yule (in 1903 ) had mentioned similar effects earlier. The name ''Simpson's paradox'' was introduced by Colin R. Blyth in 1972. It is also r ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
	Sure-thing Principle In decision theory, the sure-thing principle states that a decision maker who decided they would take a certain action in the case that event ''E'' has occurred, as well as in the case that the negation of ''E'' has occurred, should also take that same action if they know nothing about ''E''. The principle was coined by L.J. Savage:Savage, L. J. (1954), ''The foundations of statistics''. John Wiley & Sons Inc., New York. Savage formulated the principle as a dominance principle, but it can also be framed probabilistically. Richard Jeffrey and later Judea Pearl Judea Pearl (born September 4, 1936) is an Israeli-American computer scientist and philosopher, best known for championing the probabilistic approach to artificial intelligence and the development of Bayesian networks (see the article on beli ... showed that Savage's principle is only valid when the probability of the event considered (e.g., the winner of the election) is unaffected by the action (buying the propert ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
	Causal Model In the philosophy of science, a causal model (or structural causal model) is a conceptual model that describes the causal mechanisms of a system. Causal models can improve study designs by providing clear rules for deciding which independent variables need to be included/controlled for. They can allow some questions to be answered from existing observational data without the need for an interventional study such as a randomized controlled trial. Some interventional studies are inappropriate for ethical or practical reasons, meaning that without a causal model, some hypotheses cannot be tested. Causal models can help with the question of ''external validity'' (whether results from one study apply to unstudied populations). Causal models can allow data from multiple studies to be merged (in certain circumstances) to answer questions that cannot be answered by any individual data set. Causal models have found applications in signal processing, epidemiology and machine learning. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
	David Gunnell David J. Gunnell is an English epidemiologist and suicidologist who is Professor of Epidemiology at the University of Bristol. He was elected a fellow of the Academy of Medical Sciences in 2014 and received the American Foundation for Suicide Prevention's Research Award in 2015. He is also an ISI Highly Cited Researcher The Institute for Scientific Information (ISI) was an academic publishing service, founded by Eugene Garfield in Philadelphia in 1956. ISI offered scientometric and bibliographic database services. Its specialty was citation indexing and analysis, .... References External linksFaculty page British epidemiologists Suicidologists Fellows of the Academy of Medical Sciences (United Kingdom) Fellows of the Faculty of Public Health Living people Academics of the University of Bristol Alumni of the University of Bristol Year of birth missing (living people) NIHR Senior Investigators {{UK-academic-bio-stub ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
picture info	No Child Left Behind The No Child Left Behind Act of 2001 (NCLB) was a U.S. Act of Congress that reauthorized the Elementary and Secondary Education Act; it included Title I provisions applying to disadvantaged students. It supported standards-based education reform based on the premise that setting high standards and establishing measurable goals could improve individual outcomes in education. The Act required states to develop assessments in basic skills. To receive federal school funding, states had to give these assessments to all students at select grade levels. The act did not assert a national achievement standard—each state developed its own standards. NCLB expanded the federal role in public education through further emphasis on annual testing, annual academic progress, report cards, and teacher qualifications, as well as significant changes in funding. While the bill faced challenges from both Democrats and Republicans, it passed in both chambers of the legislature with significan ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]