|
Family-wise Error Rate
In statistics, family-wise error rate (FWER) is the probability of making one or more false discoveries, or type I errors when performing multiple hypotheses tests. Familywise and Experimentwise Error Rates Tukey (1953) developed the concept of a familywise error rate as the probability of making a Type I error among a specified group, or "family," of tests. Based on Tukey (1953), Ryan (1959) proposed the related concept of an ''experimentwise error rate'', which is the probability of making a Type I error in a given experiment. Hence, an experimentwise error rate is a familywise error rate for all of the tests that are conducted within an experiment. As Ryan (1959, Footnote 3) explained, an experiment may contain two or more families of multiple comparisons, each of which relates to a particular statistical inference and each of which has its own separate familywise error rate. Hence, familywise error rates are usually based on theoretically informative collections of multiple c ... [...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] |
Closed Testing Procedure
In statistics, the closed testing procedure is a general method for performing more than one hypothesis test simultaneously. The closed testing principle Suppose there are ''k'' hypotheses ''H''1,..., ''H''''k'' to be tested and the overall type I error rate is α. The closed testing principle allows the rejection of any one of these elementary hypotheses, say ''H''''i'', if all possible intersection hypotheses involving ''H''''i'' can be rejected by using valid local level α tests; the adjusted p-value is the largest among those hypotheses. It controls the family-wise error rate for all the ''k'' hypotheses at level α in the strong sense. Example Suppose there are three hypotheses ''H''1,''H''2, and ''H''3 to be tested and the overall type I error rate is 0.05. Then ''H''1 can be rejected at level α if ''H''1 ∩ ''H''2 ∩ ''H''3, ''H''1 ∩ ''H''2, ''H''1 ∩ ''H''3 and ''H''1 can all be rejected using valid tests with level α. Special cases The Holm–Bonferroni method ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistical Hypothesis Testing
A statistical hypothesis test is a method of statistical inference used to decide whether the data at hand sufficiently support a particular hypothesis. Hypothesis testing allows us to make probabilistic statements about population parameters. History Early use While hypothesis testing was popularized early in the 20th century, early forms were used in the 1700s. The first use is credited to John Arbuthnot (1710), followed by Pierre-Simon Laplace (1770s), in analyzing the human sex ratio at birth; see . Modern origins and early controversy Modern significance testing is largely the product of Karl Pearson ( ''p''-value, Pearson's chi-squared test), William Sealy Gosset ( Student's t-distribution), and Ronald Fisher ("null hypothesis", analysis of variance, "significance test"), while hypothesis testing was developed by Jerzy Neyman and Egon Pearson (son of Karl). Ronald Fisher began his life in statistics as a Bayesian (Zabell 1992), but Fisher soon grew disenchanted with t ... [...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] |
Annual Review Of Psychology
The ''Annual Review of Psychology'' is a peer-reviewed academic journal that publishes review articles about psychology. First published in 1950, its longest-serving editors have been Mark Rosenzweig (1969–1994) and Susan Fiske (2000–present). As of 2022, ''Journal Citation Reports'' gives the journal a 2021 impact factor as 27.782, ranking it first of 79 journal titles in the category "Psychology (Science)" and second of 147 titles in the category "Psychology, Multidisciplinary (Social Science)". History In 1947, the board of directors of the publishing company Annual Reviews asked a number of psychologists if it would be useful to have a journal that published an annual volume of review articles that summarized recent developments in the field. Responses were very positive, so in September 1947 they announced that the first volume of the ''Annual Review of Psychology'' would be published in 1950. Previous attempts to establish such a journal in the early 1940s were ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistical Power In statistics, the power of a binary hypothesis test is the probability that the test correctly rejects the null hypothesis (H_0) when a specific alternative hypothesis (H_1) is true. It is commonly denoted by 1-\beta, and represents the chances of a true positive detection conditional on the actual |
