The Hawthorne effect is a type of reactivity in which individuals modify an aspect of their behavior in response to their awareness of being observed. This interpretation was dubbed the Hawthorne effect, the term was coined in 1958 by Henry A. Landsberger, when analyzing earlier experiments from 1924–32 at the Hawthorne Works. The Hawthorne Works had commissioned a study to see if their workers would become more productive in higher or lower levels of light, the workers productivity seemed to improve when changes were made, and slumped when the study ended. It was suggested that the productivity gain occurred as a result of the effect on the workers of the interest being shown in them. This effect was observed for minute increases in illumination, in these lighting studies, light intensity was altered to examine its effect on worker productivity. Most industrial/occupational psychology and organizational behavior textbooks refer to the illumination studies, only occasionally are the rest of the studies mentioned.
Thus the term is used to any type of short-lived increase in productivity. H. McIlvaine Parsons defines the Hawthorne effect as the confounding that occurs if experimenters fail to realize how the consequences of subjects performance affect what subjects do, Elton Mayo describes it in terms of a positive emotional effect due to the perception of a sympathetic or interested observer. Clark and Sugrue say that uncontrolled novelty effects cause on average 30% of a standard deviation rise, studies of the demand effect suggests that people might take on pleasing the experimenter as a goal. Evaluation of the Hawthorne effect continues in the present day, in one of the studies, researchers chose two women as test subjects and asked them to choose four other workers to join the test group. Together the women worked in a room over the course of five years assembling telephone relays. Output was measured mechanically by counting how many finished relays each worker dropped down a chute and this measuring began in secret two weeks before moving the women to an experiment room and continued throughout the study.
In the experiment room they had a supervisor who discussed changes with their productivity, some of the variables were, Giving two 5-minute breaks, and changing to two 10-minute breaks. Productivity increased, but when they received six 5-minute rests, they disliked it, shortening the day by 30 minutes, shortening it more, returning to the first condition. Changing a variable usually increased productivity, even if the variable was just a back to the original condition. However it is said that this is the process of the human being adapting to the environment. Researchers concluded that the workers worked harder because they thought that they were being monitored individually, researchers hypothesized that choosing ones own coworkers, working as a group, being treated as special, and having a sympathetic supervisor were the real reasons for the productivity increase. One interpretation, mainly due to Elton Mayo, was that the six individuals became a team, the purpose of the next study was to find out how payment incentives would affect productivity
Neyman first introduced the modern concept of a confidence interval into statistical hypothesis testing and co-devised null hypothesis testing. He was born into a Polish family in Bendery, in the Bessarabia Governorate of the Russian Empire and his family was Roman Catholic and Neyman served as an altar boy during his early childhood. Later, Neyman would become an agnostic, neymans family descended from a long line of Polish nobles and military heroes. He graduated from the Kamieniec Podolski gubernial gymnasium for boys in 1909 under the name Yuri Cheslavovich Neyman and he began studies at Kharkov University in 1912, where he was taught by Russian probabilist Sergei Natanovich Bernstein. After he read Lessons on the integration and the research of the functions by Henri Lebesgue, he was fascinated with measure. In 1921 he returned to Poland in a program of repatriation of POWs after the Polish-Soviet War and he earned his Doctor of Philosophy degree at University of Warsaw in 1924 for a dissertation titled On the Applications of the Theory of Probability to Agricultural Experiments.
He was examined by Wacław Sierpiński and Stefan Mazurkiewicz, among others and he spent a couple of years in London and Paris on a fellowship to study statistics with Karl Pearson and Émile Borel. After his return to Poland he established the Biometric Laboratory at the Nencki Institute of Experimental Biology in Warsaw and he published many books dealing with experiments and statistics, and devised the way which the FDA tests medicines today. Neyman proposed and studied randomized experiments in 1923 and he introduced the confidence interval in his paper in 1937. Another noted contribution is the Neyman–Pearson lemma, the basis of hypothesis testing, in 1938 he moved to Berkeley, where he worked for the rest of his life. Thirty-nine students received their Ph. Ds under his advisorship, in 1966 he was awarded the Guy Medal of the Royal Statistical Society and three years the U. S. s National Medal of Science. He died in Oakland, California in 1981, list of Poles Fisher, Ronald Statistical methods and scientific induction Journal of the Royal Statistical Society, Series B17, 69—78.
Note on an Article by Sir Ronald Fisher, Journal of the Royal Statistical Society, Series B. Reid, Jerzy Neyman—From Life, Springer Verlag, ISBN 0-387-90747-5 OConnor, John J. Robertson, Edmund F. Jerzy Neyman, MacTutor History of Mathematics archive, University of St Andrews
Design of experiments
The design of experiments is the design of any task that aims to describe or explain the variation of information under conditions that are hypothesized to reflect the variation. In its simplest form, an experiment aims at predicting the outcome by introducing a change of the preconditions, the change in the predictor is generally hypothesized to result in a change in the second variable, hence called the outcome variable. Main concerns in design include the establishment of validity, reliability. Related concerns include achieving appropriate levels of power and sensitivity. Correctly designed experiments advance knowledge in the natural and social sciences, other applications include marketing and policy making. In 1747, while serving as surgeon on HMS Salisbury, James Lind carried out a clinical trial to compare remedies for scurvy. This systematic clinical trial constitutes a type of DOE, Lind selected 12 men from the ship, all suffering from scurvy. Lind limited his subjects to men who were as similar as I could have them and he divided them into six pairs, giving each pair different supplements to their basic diet for two weeks.
The treatments were all remedies that had proposed, A quart of cider every day. Twenty five gutts of vitriol three times a day upon an empty stomach, one half-pint of seawater every day. A mixture of garlic and horseradish in a lump the size of a nutmeg, two spoonfuls of vinegar three times a day. Two oranges and one every day. The citrus treatment stopped after six days when they ran out of fruit, apart from that, only group one showed some effect of its treatment. The remainder of the crew served as a control. Charles S. Peirce randomly assigned volunteers to a blinded, repeated-measures design to evaluate their ability to discriminate weights, peirces experiment inspired other researchers in psychology and education, which developed a research tradition of randomized experiments in laboratories and specialized textbooks in the 1800s. Charles S. Peirce contributed the first English-language publication on a design for regression models in 1876. A pioneering optimal design for regression was suggested by Gergonne in 1815.
In 1918 Kirstine Smith published optimal designs for polynomials of degree six, herman Chernoff wrote an overview of optimal sequential designs, while adaptive designs have been surveyed by S. Zacks
Statistics is a branch of mathematics dealing with the collection, interpretation and organization of data. In applying statistics to, e. g. a scientific, industrial, or social problem, populations can be diverse topics such as all people living in a country or every atom composing a crystal. Statistics deals with all aspects of data including the planning of data collection in terms of the design of surveys, statistician Sir Arthur Lyon Bowley defines statistics as Numerical statements of facts in any department of inquiry placed in relation to each other. When census data cannot be collected, statisticians collect data by developing specific experiment designs, representative sampling assures that inferences and conclusions can safely extend from the sample to the population as a whole. In contrast, an observational study does not involve experimental manipulation, inferences on mathematical statistics are made under the framework of probability theory, which deals with the analysis of random phenomena. A standard statistical procedure involves the test of the relationship between two data sets, or a data set and a synthetic data drawn from idealized model. A hypothesis is proposed for the relationship between the two data sets, and this is compared as an alternative to an idealized null hypothesis of no relationship between two data sets.
Rejecting or disproving the hypothesis is done using statistical tests that quantify the sense in which the null can be proven false. Working from a hypothesis, two basic forms of error are recognized, Type I errors and Type II errors. Multiple problems have come to be associated with this framework, ranging from obtaining a sufficient sample size to specifying an adequate null hypothesis, measurement processes that generate statistical data are subject to error. Many of these errors are classified as random or systematic, the presence of missing data or censoring may result in biased estimates and specific techniques have been developed to address these problems. Statistics continues to be an area of research, for example on the problem of how to analyze Big data. Statistics is a body of science that pertains to the collection, interpretation or explanation. Some consider statistics to be a mathematical science rather than a branch of mathematics. While many scientific investigations make use of data, statistics is concerned with the use of data in the context of uncertainty, mathematical techniques used for this include mathematical analysis, linear algebra, stochastic analysis, differential equations, and measure-theoretic probability theory.
In applying statistics to a problem, it is practice to start with a population or process to be studied. Populations can be diverse topics such as all living in a country or every atom composing a crystal. Ideally, statisticians compile data about the entire population and this may be organized by governmental statistical institutes
Terence Paul Terry Speed, FAA FRS is an Australian statistician. D. From Monash University in 1968 with thesis titled Some topics in the theory of distributive lattices under the supervision of Peter D. Finch. Speed is currently laboratory head in the Bioinformatics division at the Walter and Eliza Hall Institute of Medical Research, in Melbourne, previously, he was sharing his time between this position and the department of statistics of the University of California, Berkeley. Speed has supervised at least 67 research students, in 1989 Speed was elected as a Fellow of the American Statistical Association. Speed was president of the Institute of Mathematical Statistics in 2004, in 2002, he received the Pitman medal. In 2009 he was awarded a NHMRC Australia Fellowship, on 30 October 2013, he received the Australian Prime Ministers Prize for Science. Speed was elected a Fellow of the Royal Society of London in 2013 and his nomination reads, Speed married Freda Elizabeth Pollard in 1964. Interview with Terry Speed, by Jean Yang
Cgroups is a Linux kernel feature that limits, accounts for, and isolates the resource usage of a collection of processes. Engineers at Google started the work on feature in 2006 under the name process containers.6.24. Since then, developers have added new features and controllers, such as support for kernfs, firewalling. There are two versions of cgroups, cgroups was originally written by Paul Menage et al. and mainlined into the Linux kernel in 2007. Afterwards this is called cgroups version 1, development and maintenance of cgroups was taken over by Tejun Heo. Tejun Heo redesigned and rewrote cgroups and this rewrite is now called version 2, the documentation of cgroups-v2 first appeared in Linux kernel 4.5 released on March 14,2016. Unlike v1, cgroup v2 has only a single process hierarchy, one of the design goals of cgroups is to provide a unified interface to many different use cases, from controlling single processes to whole operating system-level virtualization. These groups can be hierarchical, meaning that each group inherits limits from its parent group, the kernel provides access to multiple controllers through the cgroup interface, for example, the memory controller limits memory use, cpuacct accounts CPU usage, etc.
Control groups can be used in ways, By accessing the cgroup virtual file system manually. By creating and managing groups on the fly using tools like cgcreate, through the rules engine daemon that can automatically move processes of certain users, groups, or commands to cgroups as specified in its configuration. Indirectly through other software that uses cgroups, such as Docker, Linux Containers virtualization, systemd, Open Grid Scheduler/Grid Engine, the Linux kernel documentation contains full technical details of the setup and use of control groups. Redesign of cgroups started in 2013, with changes brought by versions 3.15 and 3.16 of the Linux kernel. For example, a PID namespace provides a separate enumeration of process identifiers within each namespace, available are mount, UTS, network and SysV IPC namespaces. The PID namespace provides isolation for the allocation of process identifiers, lists of processes, while the new namespace is isolated from other siblings, processes in its parent namespace still see all processes in child namespaces—albeit with different PID numbers.
Network namespace isolates the network interface controllers, iptables firewall rules, network namespaces can be connected with each other using the veth virtual Ethernet device. UTS namespace allows changing the hostname, mount namespace allows creating a different file system layout, or making certain mount points read-only. IPC namespace isolates the System V inter-process communication between namespaces, user namespace isolates the user IDs between namespaces. Namespaces are created with the command or syscall, or as new flags in a clone syscall
Ian MacDougall Hacking, born February 18,1936, is a Canadian philosopher specializing in the philosophy of science. Hacking earned his PhD at Cambridge, under the direction of Casimir Lewy and he started his teaching career as an instructor at Princeton University in 1960 but, after just one year, moved to the University of Virginia as an assistant professor. After working as a fellow at Cambridge from 1962 to 1964, he taught at his alma mater, UBC, first as an assistant professor. He became a lecturer at Cambridge in 1969 before shifting to Stanford University in 1974, after teaching for several years at Stanford, he spent a year at the Center for Interdisciplinary Research in Bielefeld, from 1982 to 1983. Hacking was promoted to Professor of Philosophy at the University of Toronto in 1983 and University Professor, from 2000 to 2006, he held the Chair of Philosophy and History of Scientific Concepts at the Collège de France. Hacking is the first Anglophone to be elected to a permanent chair in the Collèges history, after retiring from the Collège de France, Hacking was a Professor of Philosophy at UC Santa Cruz, from 2008 to 2010.
He concluded his career in 2011 as a visiting professor at the University of Cape Town and currently spends his days tending to his inner-city garden in Toronto with his wife. Influenced by debates involving Thomas Kuhn, Imre Lakatos, Paul Feyerabend and others, the fourth edition of Feyerabends 1975 book Against Method, and the 50th anniversary edition of Kuhns The Structure of Scientific Revolutions include an Introduction by Hacking. He is sometimes described as a member of the Stanford School in philosophy of science, Hacking himself still identifies as a Cambridge analytic philosopher. Hacking has been a proponent of a realism about science called entity realism. This form of realism encourages a realistic stance towards answers to the scientific unknowns hypothesized by mature sciences, Hacking has been influential in directing attention to the experimental and even engineering practices of science, and their relative autonomy from theory. Because of this, Hacking moved philosophical thinking a step further than the initial historical, after 1990, Hacking shifted his focus somewhat from the natural sciences to the human sciences, partly under the influence of the work of Michel Foucault.
Foucault was an influence as early as 1975 when Hacking wrote Why Does Language Matter to Philosophy. as history, the idea of a sharp break has been criticized, but competing frequentist and subjective interpretations of probability still remain today. In Mad Travelers Hacking provided an account of the effects of a medical condition known as fugue in the late 1890s. Fugue, known as mad travel, is a type of insanity in which European men would walk in a trance for hundreds of miles without knowledge of their identities. In 2002, Hacking was awarded the first Killam Prize for the Humanities and he was made a Companion of the Order of Canada in 2004. Hacking was appointed visiting professor at University of California, Santa Cruz for the Winters of 2008 and 2009. On August 25,2009, Hacking was named winner of the Holberg International Memorial Prize, Hacking was chosen for his work on how statistics and the theory of probability have shaped society
A placebo is a substance or treatment with no active therapeutic effect. A placebo may be given to a person in order to deceive the recipient into thinking that it is an active treatment. This phenomenon, in which the recipient perceives an improvement in condition due to personal expectations, research about the effect is ongoing. Placebos are an important methodological tool in medical research, common placebos include inert tablets, vehicle infusions, sham surgery, and other procedures based on false information. Placebo effects are the subject of research aiming to understand underlying neurobiological mechanisms of action in pain relief, immunosuppression, Parkinsons disease. Brain imaging techniques done by Emeran Mayer, Johanna Jarco and Matt Lieberman showed that placebo can have real, in other cases, like asthma, the effect is purely subjective, when the patient reports improvement despite no objective change in the underlying condition. The placebo effect is a phenomenon, in fact, it is part of the response to any active medical intervention.
The placebo effect points to the importance of perception and the role in physical health. The use of placebos as treatment in clinical medicine is ethically problematic as it introduces deception, the United Kingdom Parliamentary Committee on Science and Technology has stated that. prescribing placebos. Usually relies on some degree of patient deception and prescribing pure placebos is bad medicine and their effect is unreliable and unpredictable and cannot form the sole basis of any treatment on the NHS. In 1955, Henry K. Beecher proposed that placebos could have important effects. The article received a flurry of criticism, but the published a Cochrane review with similar conclusions. A placebo has been defined as a substance or procedure and that is objectively without specific activity for the condition being treated. Under this definition, a variety of things can be placebos. Likewise, the effects of stimulation from implanted electrodes in the brains of those with advanced Parkinsons disease are greater when they are aware they are receiving this stimulation, sometimes administering or prescribing a placebo merges into fake medicine.
Common placebos include pills or saline injections, fake surgeries have seen some use. An example is the Finish Meniscal Legion Study Group’s trial published in The New England Journal of Medicine, while examples of placebo treatments can be found, defining the placebo concept remains elusive. A placebo electronic cigarette contains 0 mg of nicotine, instead they introduced the term meaning response for the meaning that the brain associates with the placebo, which causes a physiological placebo effect
International Standard Book Number
The International Standard Book Number is a unique numeric commercial book identifier. An ISBN is assigned to each edition and variation of a book, for example, an e-book, a paperback and a hardcover edition of the same book would each have a different ISBN. The ISBN is 13 digits long if assigned on or after 1 January 2007, the method of assigning an ISBN is nation-based and varies from country to country, often depending on how large the publishing industry is within a country. The initial ISBN configuration of recognition was generated in 1967 based upon the 9-digit Standard Book Numbering created in 1966, the 10-digit ISBN format was developed by the International Organization for Standardization and was published in 1970 as international standard ISO2108. Occasionally, a book may appear without a printed ISBN if it is printed privately or the author does not follow the usual ISBN procedure, this can be rectified later. Another identifier, the International Standard Serial Number, identifies periodical publications such as magazines, the ISBN configuration of recognition was generated in 1967 in the United Kingdom by David Whitaker and in 1968 in the US by Emery Koltay.
The 10-digit ISBN format was developed by the International Organization for Standardization and was published in 1970 as international standard ISO2108, the United Kingdom continued to use the 9-digit SBN code until 1974. The ISO on-line facility only refers back to 1978, an SBN may be converted to an ISBN by prefixing the digit 0. For example, the edition of Mr. J. G. Reeder Returns, published by Hodder in 1965, has SBN340013818 -340 indicating the publisher,01381 their serial number. This can be converted to ISBN 0-340-01381-8, the check digit does not need to be re-calculated, since 1 January 2007, ISBNs have contained 13 digits, a format that is compatible with Bookland European Article Number EAN-13s. An ISBN is assigned to each edition and variation of a book, for example, an ebook, a paperback, and a hardcover edition of the same book would each have a different ISBN. The ISBN is 13 digits long if assigned on or after 1 January 2007, a 13-digit ISBN can be separated into its parts, and when this is done it is customary to separate the parts with hyphens or spaces.
Separating the parts of a 10-digit ISBN is done with either hyphens or spaces, figuring out how to correctly separate a given ISBN number is complicated, because most of the parts do not use a fixed number of digits. ISBN issuance is country-specific, in that ISBNs are issued by the ISBN registration agency that is responsible for country or territory regardless of the publication language. Some ISBN registration agencies are based in national libraries or within ministries of culture, in other cases, the ISBN registration service is provided by organisations such as bibliographic data providers that are not government funded. In Canada, ISBNs are issued at no cost with the purpose of encouraging Canadian culture. In the United Kingdom, United States, and some countries, where the service is provided by non-government-funded organisations. Australia, ISBNs are issued by the library services agency Thorpe-Bowker
A scientific control is an experiment or observation designed to minimize the effects of variables other than the independent variable. This increases the reliability of the results, often through a comparison between control measurements and the other measurements, scientific controls are a part of the scientific method. Controls eliminate alternate explanations of experimental results, especially experimental errors, the selection and use of proper controls to ensure that experimental results are valid can be very difficult. Other variables, which may not be obvious, may interfere with the experimental design. For instance, the artificial sweetener might be mixed with a dilutant, to control for the effect of the dilutant, another treatment is added which is the dilutant alone. Now the experiment is controlled for the dilutant and the experimenter can distinguish between sweetener and non-treatment, controls are most often necessary where a confounding factor cannot easily be separated from the primary treatments.
For example, it may be necessary to use a tractor to spread fertilizer where there is no practicable way to spread fertilizer. The simplest solution is to have a treatment where a tractor is driven over plots without spreading fertilizer, the simplest types of control are negative and positive controls, and both are found in many different types of experiments. Negative controls are groups where no phenomenon is expected and they ensure that there is no effect when there should be no effect. To continue with the example of drug testing, a control is a group that has not been administered the drug of interest. This group receives either no preparation at all or a sham preparation and we would say that the control group should show a negative or null effect. In other examples, outcomes might be measured as lengths, percentages, in the drug testing example, we could measure the percentage of patients cured. In this case, the treatment is inferred to have no effect when the treatment group, some improvement is expected in the placebo group due to the placebo effect, and this result sets the baseline which the treatment must improve upon.
Even if the treatment group shows improvement, it needs to be compared to the placebo group, if the groups show the same effect, the treatment was not responsible for the improvement. The treatment is effective if the treatment group shows more improvement than the placebo group. Positive controls are groups where a phenomenon is expected and that is, they ensure that there is an effect when there should be an effect, by using an experimental treatment that is already known to produce that effect. Positive controls are used to assess test validity. For example, to assess a new ability to detect a disease