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Failure Bias
Failure bias is the logical error of concentrating on the people or things that failed to make it past some selection process and overlooking those that did, typically because of their lack of visibility. This can lead to false conclusions in several different ways. It is a form of selection bias. In several of these cases, one measure of success is precisely the lack of public awareness of people or things that are undergoing such selection process, which means that, in the possibility that there is at least one agent who is interested in the success of the people or things that are going through the selection process, the agent(s) will be interested into keeping the subject out of the public eye, and thus, to raise the likelihood of the failure bias happening Examples * Espionage * Most crimes fit into this category * Censorship See also * Selection bias * Cherry picking * Econometrics * '' Fooled by Randomness'' * Meta-analysis * Multiple comparisons problem Multiple com ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Logical Error
A fallacy is the use of invalid or otherwise faulty reasoning in the construction of an argument that may appear to be well-reasoned if unnoticed. The term was introduced in the Western intellectual tradition by the Aristotelian '' De Sophisticis Elenchis''. Fallacies may be committed intentionally to manipulate or persuade by deception, unintentionally because of human limitations such as carelessness, cognitive or social biases and ignorance, or potentially due to the limitations of language and understanding of language. These delineations include not only the ignorance of the right reasoning standard but also the ignorance of relevant properties of the context. For instance, the soundness of legal arguments depends on the context in which they are made. Fallacies are commonly divided into "formal" and "informal". A formal fallacy is a flaw in the structure of a deductive argument that renders the argument invalid, while an informal fallacy originates in an error in reas ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Fooled By Randomness
''Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets'' is a book by Nassim Nicholas Taleb that deals with the fallibility of human knowledge. It was first published in 2001. Updated editions were released a few years later. The book is the first part of Taleb's multi-volume philosophical essay on uncertainty, titled the '' Incerto'', which also includes '' The Black Swan'' (2007–2010), '' The Bed of Procrustes'' (2010–2016), '' Antifragile'' (2012), and '' Skin in the Game'' (2018). Thesis Taleb sets forth the idea that modern humans are often unaware of the existence of randomness. They tend to explain random outcomes as non-random. Human beings: # overestimate causality, e.g., they see elephants in the clouds instead of understanding that they are in fact randomly shaped clouds that appear to our eyes as elephants (or something else); # tend to view the world as more explainable than it really is. So they look for explanations even when there are ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Informal Fallacies
Informal fallacies are a type of incorrect argument in natural language. The source of the error is not just due to the ''form'' of the argument, as is the case for formal fallacies, but can also be due to their ''content'' and ''context''. Fallacies, despite being incorrect, usually ''appear'' to be correct and thereby can seduce people into accepting and using them. These misleading appearances are often connected to various aspects of natural language, such as ambiguous or vague expressions, or the assumption of implicit premises instead of making them explicit. Traditionally, a great number of informal fallacies have been identified, including the fallacy of equivocation, the fallacy of amphiboly, the fallacies of composition and division, the false dilemma, the fallacy of begging the question, the ad hominem fallacy and the appeal to ignorance. There is no general agreement as to how the various fallacies are to be grouped into categories. One approach sometimes found ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sampling (statistics)
In this statistics, quality assurance, and survey methodology, sampling is the selection of a subset or a statistical sample (termed sample for short) of individuals from within a population (statistics), statistical population to estimate characteristics of the whole population. The subset is meant to reflect the whole population, and statisticians attempt to collect samples that are representative of the population. Sampling has lower costs and faster data collection compared to recording data from the entire population (in many cases, collecting the whole population is impossible, like getting sizes of all stars in the universe), and thus, it can provide insights in cases where it is infeasible to measure an entire population. Each observation measures one or more properties (such as weight, location, colour or mass) of independent objects or individuals. In survey sampling, weights can be applied to the data to adjust for the sample design, particularly in stratified samplin ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Texas Sharpshooter Fallacy
The Texas sharpshooter fallacy is an informal fallacy which is committed when differences in data are ignored, but similarities are overemphasized. From this reasoning, a false conclusion is inferred. This fallacy is the philosophical or rhetorical application of the multiple comparisons problem (in statistics) and apophenia (in cognitive psychology). It is related to the clustering illusion, which is the tendency in human cognition to interpret patterns where none actually exist. The name comes from a metaphor about a person from Texas who fires a gun at the side of a barn, then paints a shooting target centered on the tightest cluster of shots and claims to be a sharpshooter. Structure The Texas sharpshooter fallacy often arises when a person has a large amount of data at their disposal but only focuses on a small subset of that data. Some factor other than the one attributed may give all the elements in that subset some kind of common property (or pair of common properties, w ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Selection Principle
In mathematics, a selection principle is a rule asserting the possibility of obtaining mathematically significant objects by selecting elements from given sequences of Set (mathematics), sets. The theory of selection principles studies these principles and their relations to other mathematical properties. Selection principles mainly describe covering properties, measure theory, measure- and category-theoretic properties, and local properties in topological spaces, especially function spaces. Often, the characterization of a mathematical property using a selection principle is a nontrivial task leading to new insights on the characterized property. The main selection principles In 1924, Karl Menger Reprinted in ''Selecta Mathematica I'' (2002), , , pp. 155-178. introduced the following basis property for metric spaces: Every basis (topology), basis of the topology contains a sequence of sets with vanishing diameters that covers the space. Soon thereafter, Witold Hurewicz ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Multiple Comparisons Problem
Multiple comparisons, multiplicity or multiple testing problem occurs in statistics when one considers a set of statistical inferences simultaneously or estimates a subset of parameters selected based on the observed values. The larger the number of inferences made, the more likely erroneous inferences become. Several statistical techniques have been developed to address this problem, for example, by requiring a stricter significance threshold for individual comparisons, so as to compensate for the number of inferences being made. Methods for family-wise error rate give the probability of false positives resulting from the multiple comparisons problem. History The problem of multiple comparisons received increased attention in the 1950s with the work of statisticians such as Tukey and Scheffé. Over the ensuing decades, many procedures were developed to address the problem. In 1996, the first international conference on multiple comparison procedures took place in Tel Aviv. T ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Meta-analysis
Meta-analysis is a method of synthesis of quantitative data from multiple independent studies addressing a common research question. An important part of this method involves computing a combined effect size across all of the studies. As such, this statistical approach involves extracting effect sizes and variance measures from various studies. By combining these effect sizes the statistical power is improved and can resolve uncertainties or discrepancies found in individual studies. Meta-analyses are integral in supporting research grant proposals, shaping treatment guidelines, and influencing health policies. They are also pivotal in summarizing existing research to guide future studies, thereby cementing their role as a fundamental methodology in metascience. Meta-analyses are often, but not always, important components of a systematic review. History The term "meta-analysis" was coined in 1976 by the statistician Gene V. Glass, Gene Glass, who stated ''"Meta-analysis refers t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Econometrics
Econometrics is an application of statistical methods to economic data in order to give empirical content to economic relationships. M. Hashem Pesaran (1987). "Econometrics", '' The New Palgrave: A Dictionary of Economics'', v. 2, p. 8 p. 8–22 Reprinted in J. Eatwell ''et al.'', eds. (1990). ''Econometrics: The New Palgrave''p. 1 p. 1–34Abstract ( 2008 revision by J. Geweke, J. Horowitz, and H. P. Pesaran). More precisely, it is "the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference." An introductory economics textbook describes econometrics as allowing economists "to sift through mountains of data to extract simple relationships." Jan Tinbergen is one of the two founding fathers of econometrics. The other, Ragnar Frisch, also coined the term in the sense in which it is used today. A basic tool for econometrics is the multiple linear regression model. ''Econome ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Selection Bias
Selection bias is the bias introduced by the selection of individuals, groups, or data for analysis in such a way that proper randomization is not achieved, thereby failing to ensure that the sample obtained is representative of the population intended to be analyzed. It is sometimes referred to as the selection effect. The phrase "selection bias" most often refers to the distortion of a statistical analysis, resulting from the method of collecting samples. If the selection bias is not taken into account, then some conclusions of the study may be false. Types of bias Sampling bias Sampling bias is systematic error due to a non-random sample of a population, causing some members of the population to be less likely to be included than others, resulting in a biased sample, defined as a statistical sample of a statistical population, population (or non-human factors) in which all participants are not equally balanced or objectively represented. It is mostly classified as a subtype of ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cherry Picking
Cherry picking, suppressing evidence, or the fallacy of incomplete evidence is the act of pointing to individual cases or data that seem to confirm a particular position while ignoring a significant portion of related and similar cases or data that may contradict that position. Cherry picking may be committed intentionally or unintentionally. The term is based on the perceived process of harvesting fruit, such as cherries. The picker would be expected to select only the ripest and healthiest fruits. An observer who sees only the selected fruit may thus wrongly conclude that most, or even all, of the tree's fruit is in a likewise good condition. This can also give a false impression of the quality of the fruit (since it is only a sample and is not a representative sample). A concept sometimes confused with cherry picking is the idea of gathering only the fruit that is easy to harvest, while ignoring other fruit that is higher up on the tree and thus more difficult to obtain (see ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Selection Bias
Selection bias is the bias introduced by the selection of individuals, groups, or data for analysis in such a way that proper randomization is not achieved, thereby failing to ensure that the sample obtained is representative of the population intended to be analyzed. It is sometimes referred to as the selection effect. The phrase "selection bias" most often refers to the distortion of a statistical analysis, resulting from the method of collecting samples. If the selection bias is not taken into account, then some conclusions of the study may be false. Types of bias Sampling bias Sampling bias is systematic error due to a non-random sample of a population, causing some members of the population to be less likely to be included than others, resulting in a biased sample, defined as a statistical sample of a statistical population, population (or non-human factors) in which all participants are not equally balanced or objectively represented. It is mostly classified as a subtype of ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
