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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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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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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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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Descriptive statistics summarize features of a sample, such as mean and standard deviations, median and quartiles, the maximum and minimum. With multiple variables, may include correlations and crosst…
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ordering data from highest to lowest or *vice versa.* For questions about *constructing* scores to use in ranking, please use the "valuation" tag, too.
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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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Refers to techniques for classifying data into categories based on similarities (which can either be known previously, or learned).
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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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Generalized Linear Mixed (effects) Models are typically used for modeling non-independent non-normal data (eg, longitudinal binary data).
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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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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 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 any statistical complication or problem due to having few data.
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Refers to the probability distribution of parameters conditioned on data in Bayesian statistics.
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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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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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Distance functions refer to functions used for quantifying the notion of distance between members of a set, or between objects.
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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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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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a graphical representation of the frequencies of a continuous variable. The variable is divided into bins and a bar is drawn for each bin, proportional to its frequency in the data.
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Skewness measures (or refers to) a degree of asymmetry in the distribution of a variable.
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one whose joint distribution is constant over time. A weakly stationary process or series is one whose mean and covariance function (variance and autocorrelati…
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The science of statistics applied to the analysis of biological or medical data.
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Causal inference tries to quantify the effect of a change in $X$ on $Y$ whilst holding constant or eliminating all other relevant factors which might influence this relationship.
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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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Psychometrics has evolved as a subfield of psychology to become the science of measurement of unobservable individual characteristics.
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an integrated software library for support vector machines, performing support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class…
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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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the distribution of a random variable whose logarithm has a normal distribution.
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one that is written as a convex combination of other distributions.