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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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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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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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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Group differences broadly refer to statistics which quantify the differences between two or more subpopulations.
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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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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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Robustness in general refers to a statistic's insensitivity to deviations from its underlying assumptions (Huber and Ronchetti, 2009).
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Refers to the probability distribution of parameters conditioned on data in Bayesian statistics.
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Skewness measures (or refers to) a degree of asymmetry in the distribution of a variable.
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Machine learning framework for Python.
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express measurements, usually ratio, interval, ordinal or nominal scales.
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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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'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 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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Refers to any statistical complication or problem due to having few data.
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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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Refers to techniques for classifying data into categories based on similarities (which can either be known previously, or learned).
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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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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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The science of statistics applied to the analysis of biological or medical data.
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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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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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fit curves (as in linear or non-linear regression) to data.
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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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the value below which half the data or probability distribution lies - when the sample size is odd, the median is the 'middle' value of an ordered sample.
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an R package to fit linear and generalized linear mixed-effects models.
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Combining probabilities with Bayes' Theorem, especially as used for conditional inference.
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the distribution of a random variable whose logarithm has a normal distribution.
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Measure of distance between distributions or variables, such as Euclidean distance between points in n-space.
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an acronym for cumulative distribution function. While the pdf gives the probability density of each value of a random variable, the cdf (often denoted F(x)) gives the probability that the rand…