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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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Shifting and rescaling data to assure zero mean and unit variance.
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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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Group differences broadly refer to statistics which quantify the differences between two or more subpopulations.
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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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decided upon after the data has been collected, as opposed to "a priori".
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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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Refers to the probability distribution of parameters conditioned on data in Bayesian statistics.
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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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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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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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'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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express measurements, usually ratio, interval, ordinal or nominal scales.
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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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Skewness measures (or refers to) a degree of asymmetry in the distribution of a variable.
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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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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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Distance functions refer to functions used for quantifying the notion of distance between members of a set, or between objects.
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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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Refers to any statistical complication or problem due to having few data.
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The science of statistics applied to the analysis of biological or medical 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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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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a generic tag for requests of any kind of resources: books, textbooks, manuals, papers, presentations, video lectures, scripts, etc.
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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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Measure of distance between distributions or variables, such as Euclidean distance between points in n-space.
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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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fit curves (as in linear or non-linear regression) to data.
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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…
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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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Combining probabilities with Bayes' Theorem, especially as used for conditional inference.