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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Convergence generally means that a sequence of a certain sample quantity approaches a constant as the sample size tends to infinity.
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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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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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Kernel trick refers to kernel methods in machine learning, such as kernel support vector machine (SVM) or kernel principal components analysis (PCA). It allows to generalize linear techniques to non-l…
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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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Group differences broadly refer to statistics which quantify the differences between two or more subpopulations.
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decided upon after the data has been collected, as opposed to "a priori".
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an R package to fit linear and generalized linear mixed-effects models.
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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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Machine learning framework for Python.
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Skewness measures (or refers to) a degree of asymmetry in the distribution of a variable.
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Shifting and rescaling data to assure zero mean and unit variance.
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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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a new area of Machine Learning research concerned with the technologies used for learning hierarchical representations of data, mainly done with deep neural networks (i.e. networks with two or more hi…
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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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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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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 the probability distribution of parameters conditioned on data in Bayesian statistics.
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express measurements, usually ratio, interval, ordinal or nominal scales.
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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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Combining probabilities with Bayes' Theorem, especially as used for conditional inference.
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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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Refers to any statistical complication or problem due to having few data.
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fit curves (as in linear or non-linear regression) to data.
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The science of statistics applied to the analysis of biological or medical data.
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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 method of estimating a probability distribution using estimators of a particular form.
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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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Refers to techniques for classifying data into categories based on similarities (which can either be known previously, or learned).
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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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Given a random variable $X$ which arise from a parameterized distribution $F(X;θ)$, the likelihood is defined as the probability of observed data as a function of $θ: \text{L}(θ)=\text{P}(θ;X=x)$
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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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a stochastic process modelling time series, which specifies the value of the series linearly in terms of the previous values.
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Measure of distance between distributions or variables, such as Euclidean distance between points in n-space.