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Insensitivity To Sample Size
Insensitivity to sample size is a cognitive bias that occurs when people judge the probability of obtaining a sample statistic without respect to the sample size. For example, in one study subjects assigned the same probability to the likelihood of obtaining a mean height of above six feet 83 cmin samples of 10, 100, and 1,000 men. In other words, variation is more likely in smaller samples, but people may not expect this. In another example, Amos Tversky and Daniel Kahneman asked subjects A certain town is served by two hospitals. In the larger hospital about 45 babies are born each day, and in the smaller hospital about 15 babies are born each day. As you know, about 50% of all babies are boys. However, the exact percentage varies from day to day. Sometimes it may be higher than 50%, sometimes lower. For a period of 1 year, each hospital recorded the days on which more than 60% of the babies born were boys. Which hospital do you think recorded more such days? # The larger ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cognitive Bias
A cognitive bias is a systematic pattern of deviation from norm or rationality in judgment. Individuals create their own "subjective reality" from their perception of the input. An individual's construction of reality, not the objective input, may dictate their behavior in the world. Thus, cognitive biases may sometimes lead to perceptual distortion, inaccurate judgment, illogical interpretation, or what is broadly called irrationality. Although it may seem like such misperceptions would be aberrations, biases can help humans find commonalities and shortcuts to assist in the navigation of common situations in life. Some cognitive biases are presumably adaptive. Cognitive biases may lead to more effective actions in a given context. Furthermore, allowing cognitive biases enables faster decisions which can be desirable when timeliness is more valuable than accuracy, as illustrated in heuristics. Other cognitive biases are a "by-product" of human processing limitations, resulting ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sample Statistic
A statistic (singular) or sample statistic is any quantity computed from values in a sample which is considered for a statistical purpose. Statistical purposes include estimating a population parameter, describing a sample, or evaluating a hypothesis. The average (or mean) of sample values is a statistic. The term statistic is used both for the function and for the value of the function on a given sample. When a statistic is being used for a specific purpose, it may be referred to by a name indicating its purpose. When a statistic is used for estimating a population parameter, the statistic is called an ''estimator''. A population parameter is any characteristic of a population under study, but when it is not feasible to directly measure the value of a population parameter, statistical methods are used to infer the likely value of the parameter on the basis of a statistic computed from a sample taken from the population. For example, the sample mean is an unbiased estimator of t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sample Size
Sample size determination is the act of choosing the number of observations or Replication (statistics), replicates to include in a statistical sample. The sample size is an important feature of any empirical study in which the goal is to make statistical inference, inferences about a statistical population, population from a sample. In practice, the sample size used in a study is usually determined based on the cost, time, or convenience of collecting the data, and the need for it to offer sufficient statistical power. In complicated studies there may be several different sample sizes: for example, in a stratified sampling, stratified survey sampling, survey there would be different sizes for each stratum. In a census, data is sought for an entire population, hence the intended sample size is equal to the population. In experimental design, where a study may be divided into different treatment groups, there may be different sample sizes for each group. Sample sizes may be chosen in ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Amos Tversky
Amos Nathan Tversky ( he, עמוס טברסקי; March 16, 1937 – June 2, 1996) was an Israeli cognitive and mathematical psychologist and a key figure in the discovery of systematic human cognitive bias and handling of risk. Much of his early work concerned the foundations of measurement. He was co-author of a three-volume treatise, ''Foundations of Measurement''. His early work with Daniel Kahneman focused on the psychology of prediction and probability judgment; later they worked together to develop prospect theory, which aims to explain irrational human economic choices and is considered one of the seminal works of behavioral economics. Six years after Tversky's death, Kahneman received the 2002 Nobel Memorial Prize in Economic Sciences for the work he did in collaboration with Amos Tversky. (The prize is not awarded posthumously.) Kahneman told ''The New York Times'' in an interview soon after receiving the honor: "I feel it is a joint prize. We were twinned for ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Daniel Kahneman
Daniel Kahneman (; he, דניאל כהנמן; born March 5, 1934) is an Israeli-American psychologist and economist notable for his work on the psychology of judgment and decision-making, as well as behavioral economics, for which he was awarded the 2002 Nobel Memorial Prize in Economic Sciences (shared with Vernon L. Smith). His empirical findings challenge the assumption of human rationality prevailing in modern economic theory. With Amos Tversky and others, Kahneman established a cognitive basis for common human errors that arise from heuristics and biases, and developed prospect theory. In 2011 he was named by '' Foreign Policy'' magazine in its list of top global thinkers. In the same year his book ''Thinking, Fast and Slow'', which summarizes much of his research, was published and became a best seller. In 2015, ''The Economist'' listed him as the seventh most influential economist in the world. He is professor emeritus of psychology and public affairs at Princeton U ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistical Sampling
In statistics, quality assurance, and survey methodology, sampling is the selection of a subset (a statistical sample) of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt to collect samples that are representative of the population in question. Sampling has lower costs and faster data collection than measuring the entire population and 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 sampling. Results from probability theory and statistical theory are employed to guide the practice. In business and medical research, sampling is widely used for gathering information about a population. Acceptance sampling is used to determi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Law Of Large Numbers
In probability theory, the law of large numbers (LLN) is a theorem that describes the result of performing the same experiment a large number of times. According to the law, the average of the results obtained from a large number of trials should be close to the expected value and tends to become closer to the expected value as more trials are performed. The LLN is important because it guarantees stable long-term results for the averages of some random events. For example, while a casino may lose money in a single spin of the roulette wheel, its earnings will tend towards a predictable percentage over a large number of spins. Any winning streak by a player will eventually be overcome by the parameters of the game. Importantly, the law applies (as the name indicates) only when a ''large number'' of observations are considered. There is no principle that a small number of observations will coincide with the expected value or that a streak of one value will immediately be "balanced ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Representativeness Heuristic
The representativeness heuristic is used when making judgments about the probability of an event under uncertainty. It is one of a group of heuristics (simple rules governing judgment or decision-making) proposed by psychologists Amos Tversky and Daniel Kahneman in the early 1970s as "the degree to which n event(i) is similar in essential characteristics to its parent population, and (ii) reflects the salient features of the process by which it is generated". Heuristics are described as "judgmental shortcuts that generally get us where we need to go – and quickly – but at the cost of occasionally sending us off course." Heuristics are useful because they use effort-reduction and simplification in decision-making. When people rely on representativeness to make judgments, they are likely to judge wrongly because the fact that something is more representative does not actually make it more likely. The representativeness heuristic is simply described as assessing similarity of ob ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Clustering Illusion
The clustering illusion is the tendency to erroneously consider the inevitable "streaks" or "clusters" arising in small samples from random distributions to be non-random. The illusion is caused by a human tendency to underpredict the amount of variability likely to appear in a small sample of random or pseudorandom data. Examples Thomas Gilovich, an early author on the subject, argued that the effect occurs for different types of random dispersions, including two-dimensional data such as clusters in the locations of impact of World War II V-1 flying bombs on maps of London; or seeing patterns in stock market price fluctuations over time. Although Londoners developed specific theories about the pattern of impacts within London, a statistical analysis by R. D. Clarke originally published in 1946 showed that the impacts of V-2 rockets on London were a close fit to a random distribution. Similar biases Using this cognitive bias in causal reasoning may result in the Texas sharp ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Extension Neglect
__NOTOC__ Extension neglect is a type of cognitive bias which occurs when the sample size is ignored when its determination is relevant. For instance, when reading an article about a scientific study, extension neglect occurs when the reader ignores the number of people involved in the study (sample size) but still makes inferences about a population based on the sample. In reality, if the sample size is too small, the results might risk errors in statistical hypothesis testing. A study based on only a few people may draw invalid conclusions because only one person has exceptionally high or low scores (outlier), and there are not enough people there to correct this via averaging out. But often, the sample size is not prominently displayed in science articles, and the reader in this case might still believe the article's conclusion due to extension neglect. Extension neglect is described as being caused by judgment by prototype, of which the representativeness heuristic is a special ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Howard Wainer
Howard Wainer (born 1943) is an American statistician, past principal research scientist at the Educational Testing Service, adjunct professor of statistics at the Wharton School of the University of Pennsylvania, and author, known for his contributions in the fields of statistics, psychometrics, and statistical graphics. Biography Early life Howard Wainer was born Howard Charles Goldhaber in Brooklyn, New York on October 26, 1943. In 1948 his father Meyer Goldhaber, an anatomist by education and a dentist by profession, died of complications from a bleeding ulcer at the age of 35. Howard, his brother and his mother moved in with his mother's parents. After two years his mother married Sam Wainer, a local businessman, and the family relocated to Long Island. Howard was formally adopted by his mother's new husband and took the surname Wainer. Education Early on Wainer showed an aptitude for science and mathematics. In 1960, at the end of his junior year in high school, he was acce ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
