A tag is a keyword or label that categorizes your question with other, similar questions. Using the right tags makes it easier for others to find and answer your question.

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a subset of a population. Statistics, in general, is concerned with using samples to make inference about the parameters governing a larger (possibly infinite) population.
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independent when information on some of them tells you nothing about the probability of occurrence (/ distribution) of the others. Please DO NOT use this tag for indep…
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Multicollinearity means predictor variables are correlated with each other, making it harder to determine the role each of the correlated variables is playing. Mathematically, it means the standard er…
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a family of cluster analysis methods in which you specify the number of clusters you expect. This is as opposed to hierarchical cluster analysis methods.
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Kernel refers to weighting functions used in non-parametric estimation techniques (such as kernel density estimation or kernel smoothing). DO NOT USE this tag for [kernel-trick] which is reserved for …
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a rectangular array of numbers, symbols, or expressions arranged in rows and columns. The individual items in a matrix are called its elements or entries.
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AIC stands for the Akaike Information Criterion, which is one technique used to select the best model from a class of models using a penalized likelihood. A smaller AIC implies a better model.
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An inquiry into the quality of a statistical test by calculating the power - the probability of rejecting the null hypothesis given that it is false - under certain circumstances. Power analysis is of…
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A stochastic process with the property that the future is conditionally independent of the past, given the present.
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Refers to the conditions under which a statistics procedure yields valid estimates and/or inference. E.g., many statistical techniques require the assumption that the data are randomly sampled in some…
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A brief numerical description of a set of data.
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Meta-analysis refers to methods focused on contrasting and combining results from different studies, in the hope of identifying patterns among study results, sources of disagreement among those result…
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Refers to the normal distribution, the Gaussian continuous probability distribution.
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The act of generating a sequence of numbers or symbols randomly, or (more often) pseudo-randomly; i.e., with lack of any predictability or pattern.
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a multivariate technique popular in social sciences. It is based on formulating a set of linear relations between variables, some of which may be latent, and estimating…
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In broader sense - synonym of "dichotomous data": any data that can take on only one of two values. In narrower sense - dichotomous data coded as 1 or 0; furthermore, sometimes "1" is supposed to mean…
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Dimensionality reduction refers to techniques for reducing many variables into a smaller number while keeping as much information as possible. One prominent method is [tag pca]
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a commercial spreadsheet program created by Microsoft.
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Receiver Operating Characteristic, also known as ROC curve.
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said to have a high reliability if it produces similar results under consistent conditions. DO NOT confuse reliability with validity (see tag wiki).
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"a measure of the strength of a phenomenon or a sample-based estimate of that quantity" [Wikipedia].
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Refers to the interface of statistics and computing; the use of algorithms and software for statistical purposes.
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A name given to the log-odds function, which maps probabilities to the real line.
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used for modelling systems that are assumed to be Markov processes with hidden (i.e. unobserved) states.
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In linear regression, the coefficient of determination, usually symbolized by $R^2$, is the proportion of the total response variance explained by the regression model.
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Analysis of Covariance.
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non-negative integers representing whole amounts. When such data are the dependent variable in a regression, Poisson or negative binomial regression may be appropriate methods. One comm…
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Bias, in a statistical framework, means that an estimate of a parameter has an expected value that is not equal to the actual parameter value. There is often a tradeoff between bias and variance - low…
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Group differences broadly refer to statistics which quantify the differences between two or more subpopulations.
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In Bayesian statistics a prior distribution formalizes information or knowledge (often subjective), available before a sample is seen, in the form of a probability distribution. A distribution with la…
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"Mixed effects models" refers to models that have both fixed effects and random effects. They are used to model longitudinal data or data that are clustered & thus do not have independent errors.
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a generic tag for requests of any kind of resources: books, textbooks, manuals, papers, presentations, video lectures, scripts, etc.
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A non-negative continuous probability distribution indexed by two strictly positive parameters.
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Is a property of a hypothesis testing method: the probability of rejecting the null hypothesis given that it is false, i.e. the probability of not making a type II error. The power of a test depends o…
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A discrete, univariate distribution modelling the number of ${\rm Bernoulli}(p)$ trial successes until a specified number of failures occur.