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Sequential Analysis
In statistics, sequential analysis or sequential hypothesis testing is statistical analysis where the sample size is not fixed in advance. Instead data are evaluated as they are collected, and further sampling is stopped in accordance with a pre-defined stopping rule as soon as significant results are observed. Thus a conclusion may sometimes be reached at a much earlier stage than would be possible with more classical hypothesis testing or estimation, at consequently lower financial and/or human cost. History The method of sequential analysis is first attributed to Abraham Wald with Jacob Wolfowitz, W. Allen Wallis, and Milton Friedman while at Columbia University's Statistical Research Group as a tool for more efficient industrial quality control during World War II. Its value to the war effort was immediately recognised, and led to its receiving a "restricted" classification. At the same time, George Barnard led a group working on optimal stopping in Great Britain. Ano ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistics
Statistics (from German language, German: ''wikt:Statistik#German, Statistik'', "description of a State (polity), state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied. Populations can be diverse groups of people or objects such as "all people living in a country" or "every atom composing a crystal". Statistics deals with every aspect of data, including the planning of data collection in terms of the design of statistical survey, surveys and experimental design, experiments.Dodge, Y. (2006) ''The Oxford Dictionary of Statistical Terms'', Oxford University Press. When census data cannot be collected, statisticians collect data by developing specific experiment designs and survey sample (statistics), samples. Representative sampling as ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Alan Turing
Alan Mathison Turing (; 23 June 1912 – 7 June 1954) was an English mathematician, computer scientist, logician, cryptanalyst, philosopher, and theoretical biologist. Turing was highly influential in the development of theoretical computer science, providing a formalisation of the concepts of algorithm and computation with the Turing machine, which can be considered a model of a general-purpose computer. He is widely considered to be the father of theoretical computer science and artificial intelligence. Born in Maida Vale, London, Turing was raised in southern England. He graduated at King's College, Cambridge, with a degree in mathematics. Whilst he was a fellow at Cambridge, he published a proof demonstrating that some purely mathematical yes–no questions can never be answered by computation and defined a Turing machine, and went on to prove that the halting problem for Turing machines is undecidable. In 1938, he obtained his PhD from the Department of Mathemati ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Christiaan Huygens
Christiaan Huygens, Lord of Zeelhem, ( , , ; also spelled Huyghens; la, Hugenius; 14 April 1629 – 8 July 1695) was a Dutch mathematician, physicist, engineer, astronomer, and inventor, who is regarded as one of the greatest scientists of all time and a major figure in the Scientific Revolution. In physics, Huygens made groundbreaking contributions in optics and mechanics, while as an astronomer he is chiefly known for his studies of the rings of Saturn and the discovery of its moon Titan. As an engineer and inventor, he improved the design of telescopes and invented the pendulum clock, a breakthrough in timekeeping and the most accurate timekeeper for almost 300 years. An exceptionally talented mathematician and physicist, Huygens was the first to idealize a physical problem by a set of mathematical parameters, and the first to fully mathematize a mechanistic explanation of an unobservable physical phenomenon.Dijksterhuis, F.J. (2008) Stevin, Huygens and the Dutch republ ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Gambler's Ruin
The gambler's ruin is a concept in statistics. It is most commonly expressed as follows: A gambler playing a game with negative expected value will eventually go broke, regardless of their betting system. The concept was initially stated: A persistent gambler who raises his or her bet to a fixed fraction of the gambler's bankroll after a win, but does not reduce it after a loss, will eventually and inevitably go broke, even if each bet has a positive expected value. Another statement of the concept is that a persistent gambler with finite wealth, playing a fair game (that is, each bet has expected value of zero to both sides) will eventually and inevitably go broke against an opponent with infinite wealth. Such a situation can be modeled by a random walk on the real number line. In that context, it is probable that the gambler will, with virtual certainty, return to his or her point of origin, which means going broke, and is ruined an infinite number of times if the random walk co ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Jones And Bartlett Publishers
Jones & Bartlett Learning, a division of Ascend Learning, is a scholarly publisher. The name comes from Donald W. Jones, the company's founder, and Arthur Bartlett, the first editor. History In 1988, the company was named by ''New England Business Magazine'' as one of the 100 fastest-growing companies in New England. In 1989, they opened their first office in London. In 1993, they opened an office in Singapore, and an office in Toronto in 1994. Their corporate headquarters moved to Sudbury, Massachusetts in 1995. In 2011, Jones & Bartlett Learning moved its offices in Sudbury and Maynard, Massachusetts to Burlington, Massachusetts, sharing a building with other Ascend Learning corporate offices. See also * National Healthcareer Association * DVP Media DVP may refer to: * ''decessit vita patris'', "died in the lifetime of his father", term used by genealogists to denote a child who pre-deceased his or her father and did not live long enough to inherit the father's title or es ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Null Hypothesis
In scientific research, the null hypothesis (often denoted ''H''0) is the claim that no difference or relationship exists between two sets of data or variables being analyzed. The null hypothesis is that any experimentally observed difference is due to chance alone, and an underlying causative relationship does not exist, hence the term "null". In addition to the null hypothesis, an alternative hypothesis is also developed, which claims that a relationship does exist between two variables. Basic definitions The ''null hypothesis'' and the ''alternative hypothesis'' are types of conjectures used in statistical tests, which are formal methods of reaching conclusions or making decisions on the basis of data. The hypotheses are conjectures about a statistical model of the population, which are based on a sample of the population. The tests are core elements of statistical inference, heavily used in the interpretation of scientific experimental data, to separate scientific claims fr ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Haybittle–Peto Boundary
The Haybittle–Peto boundary is a rule for deciding when to stop a clinical trial prematurely. It is named for John Haybittle and Richard Peto. The typical clinical trial compares two groups of patients. One group are given a placebo or conventional treatment, while the other group of patients are given the treatment that is being tested. The investigators running the clinical trial will wish to stop the trial early for ethical reasons if the treatment group clearly shows evidence of benefit. In other words, "when early results proved so promising it was no longer fair to keep patients on the older drugs for comparison, without giving them the opportunity to change." The Haybittle–Peto boundary is one such stopping rule, and it states that if an interim analysis shows a probability of equal to, or less than 0.001 that a difference as extreme or more between the treatments is found, given that the null hypothesis is true, then the trial should be stopped early. The final analysi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Pocock Boundary
The Pocock boundary is a method for determining whether to stop a clinical trial prematurely. The typical clinical trial compares two groups of patients. One group are given a placebo or conventional treatment, while the other group of patients are given the treatment that is being tested. The investigators running the clinical trial will wish to stop the trial early for ethical reasons if the treatment group clearly shows evidence of benefit. In other words, "when early results proved so promising it was no longer fair to keep patients on the older drugs for comparison, without giving them the opportunity to change." The concept was introduced by the medical statistician Stuart Pocock in 1977. The many reasons underlying when to stop a clinical trial for benefit were discussed in his editorial from 2005. Details The Pocock boundary gives a ''p''-value threshold for each interim analysis which guides the data monitoring committee on whether to stop the trial. The boundary used d ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bonferroni Correction
In statistics, the Bonferroni correction is a method to counteract the multiple comparisons problem. Background The method is named for its use of the Bonferroni inequalities. An extension of the method to confidence intervals was proposed by Olive Jean Dunn. Statistical hypothesis testing is based on rejecting the null hypothesis if the likelihood of the observed data under the null hypotheses is low. If multiple hypotheses are tested, the probability of observing a rare event increases, and therefore, the likelihood of incorrectly rejecting a null hypothesis (i.e., making a Type I error) increases. The Bonferroni correction compensates for that increase by testing each individual hypothesis at a significance level of \alpha/m, where \alpha is the desired overall alpha level and m is the number of hypotheses. For example, if a trial is testing m = 20 hypotheses with a desired \alpha = 0.05, then the Bonferroni correction would test each individual hypothesis at \alpha = 0.05/20 = ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Type 1 Error
In statistical hypothesis testing, a type I error is the mistaken rejection of an actually true null hypothesis (also known as a "false positive" finding or conclusion; example: "an innocent person is convicted"), while a type II error is the failure to reject a null hypothesis that is actually false (also known as a "false negative" finding or conclusion; example: "a guilty person is not convicted"). Much of statistical theory revolves around the minimization of one or both of these errors, though the complete elimination of either is a statistical impossibility if the outcome is not determined by a known, observable causal process. By selecting a low threshold (cut-off) value and modifying the alpha (α) level, the quality of the hypothesis test can be increased. The knowledge of type I errors and type II errors is widely used in medical science, biometrics and computer science. Intuitively, type I errors can be thought of as errors of ''commission'', i.e. the researcher unluck ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Stuart Pocock
Stuart J. Pocock is a British medical statistician. He has been professor of medical statistics at the London School of Hygiene and Tropical Medicine since 1989. His research interests include statistical methods for the design, monitoring, analysis and reporting of randomized clinical trials. He also collaborates on major clinical trials, particularly in cardiovascular disease. In 2003, the Royal Statistical Society awarded him the Bradford Hill Medal "for his development of clinical trials methodology, including group sequential methods, his extensive applied work, notably in the epidemiology Epidemiology is the study and analysis of the distribution (who, when, and where), patterns and determinants of health and disease conditions in a defined population. It is a cornerstone of public health, and shapes policy decisions and evidenc ... and treatment of heart disease, and his exposition of good practice nationally and internationally, especially through his book ''Clinical T ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Peter Armitage (statistician)
Peter Armitage CBE (born 15 June 1924) is a statistician specialising in medical statistics. Peter Armitage attended Huddersfield College and went on to read mathematics at Trinity College, Cambridge. Armitage belonged to the generation of mathematicians who came to maturity in the Second World War. He joined the weapons procurement agency, the Ministry of Supply where he worked on statistical problems with George Barnard. After the war he resumed his studies and then worked as a statistician for the Medical Research Council from 1947 to 1961. From 1961 to 1976, he was Professor of Medical Statistics at the London School of Hygiene and Tropical Medicine where he succeeded Austin Bradford Hill. His main work there was on sequential analysis. He moved to Oxford as Professor of Biomathematics and became Professor of Applied Statistics and head of the new Department of Statistics, retiring in 1990. He was president of the Royal Statistical Society in 1982–4. He was president of ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
