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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called continuous if its set of possible values is uncountable, and the chance that it takes any particular value is zero ($\text{P}(X = x) = 0$ for every real number $x$). A …
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a simple probabilistic classifier based on applying Bayes' theorem with strong independence assumptions. A more descriptive term for the underlying probability model would …
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a set of techniques from linguistics, artificial intelligence, machine learning and statistics that aim at processing and understanding human languages.
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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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really a special case of multiple linear regression, used in ANOVA-like settings with some continuous covariates in addition to the categorical ones.
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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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used in machine learning to generalize linear techniques to nonlinear situations, especially SVMs, PCA, and GPs. Not to be confused with [kernel-smoothing], for kernel density estim…
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Generalized Linear Mixed (effects) Models are typically used for modeling non-independent non-normal data (eg, longitudinal binary data).
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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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Refers to any statistical complication or problem due to having few data.
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Measure of distance between distributions or variables, such as Euclidean distance between points in n-space.
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Group differences broadly refer to statistics which quantify the differences between two or more subpopulations.
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Combining probabilities with Bayes' Theorem, especially as used for conditional inference.
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The relationship between cause and effect.
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a stochastic process modelling time series, which specifies the value of the series linearly in terms of the previous values.
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A $k\times k$ matrix of covariances between all pairs of $k$ random variables. It is also called variance-covariance matrix or simply variance matrix.
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Skewness measures (or refers to) a degree of asymmetry in the distribution of a variable.
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Gaussian processes refer to stochastic processes whose realization consists of normally distributed random variables, with the additional property that any finite collection of these random variables …
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a commercial spreadsheet program created by Microsoft.
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The science of statistics applied to the analysis of biological or medical data.
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Shifting and rescaling data to assure zero mean and unit variance.
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decided upon after the data has been collected, as opposed to "a priori".
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Kernel smoothing techniques, such as kernel density estimation (KDE) and Nadaraya-Watson kernel regression, estimate functions by local interpolation from data points. Not to be confused with [kernel-…
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Refers to the probability distribution of parameters conditioned on data in Bayesian statistics.
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Inclusion of additional terms (typically a penalty for complexity) in the model fitting process. Used to prevent overfitting / enhance predictive accuracy.
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Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models are used for time series in which the residual variance changes over time. The variance of the error term is assumed to follow …
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A distribution describing the time between events in a Poisson process; a continuous analogue of the geometric distribution.
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The quantiles of a distribution refer to points on its cumulative distribution function. Some common quantiles are quartiles and percentiles.
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Robustness in general refers to a statistic's insensitivity to deviations from its underlying assumptions (Huber and Ronchetti, 2009).
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'Large data' refers to situations where the number of observations (data points) is so large that it necessitates changes in the way the data analyst thinks about or conducts the analysis. (Not to be …
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a test for goodness of fit of data to a distribution. It is often used to test whether a variable is normally distributed.
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Refers to data generated from a distribution that has a countable sample space. Discrete data may be nominal (e.g. the distribution of race in a sample of individuals) or ordinal (e.g. the number of e…
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the process of assessing whether the results of an analysis are likely to hold outside of the original research setting. DO NOT use this tag for discussing `validity` of a measurement or…
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The uniform distribution describes a random variable that is equally likely to take any value in its sample space.
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Average most often refers to the arithmetic mean, but more generally to measures of central tendency that use most, or all, of the data values. Examples include trimmed mean, Winsorized mean, harmonic…
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fit curves (as in linear or non-linear regression) to data.