Our Latest Blogs

To some, NLP = natural language processing, a form of text analytics arising from the field of computational linguistics.

As applied to statistical models - "overfit" means the model is too accurate, and fitting noise, not signal. For example, the complex polynomial curve in the figure fits the data with no error, but you would not want to rely on it to predict accurately for new data:

Logit is a nonlinear function of probability. If p is the probability of an event, then the corresponding logit is given by the formula: logit(p) = log  p (1 - p)   Logit is widely used to construct statistical models, for example in logistic regression. 

Intraobserver reliability indicates how stable are responses obtained from the same respondent at different time points. The greater the difference between the responses, the smaller the intraobserver reliability of the survey instrument. The correlation coefficient between the responses obtained at different time points from the same respondent is often…

Two events A and B are said to be independent if P(A?B) = P(A).P(B). To put it differently, events A and B are independent if the occurrence or non-occurrence of A does not influence the occurrence of non-occurrence of B and vice-versa. For example, if…

Residuals are differences between the observed values and the values predicted by some model. Analysis of residuals allows you to estimate the adequacy of a model for particular data; it is widely used in regression analysis. 

The concurrent validity of survey instruments, like the tests used in psychometrics , is a measure of agreement between the results obtained by the given survey instrument and the results obtained for the same population by another instrument acknowledged as the "gold standard". The concurrent validity is often quantified by the correlation…

Normality is a property of a random variable that is distributed according to the normal distribution. Normality plays a central role in both theoretical and practical statistics: a great number of theoretical statistical methods rest on the assumption that the data, or test statistics derived from…

In statistics, "n" denotes the size of a dataset, typically a sample, in terms of the number of observations or records.

A corpus is a body of documents to be used in a text mining task.  Some corpuses are standard public collections of documents that are commonly used to benchmark and tune new text mining algorithms.  More typically, the corpus is a body of documents for…

Weighted kappa is a measure of agreement for Categorical data . It is a generalization of the Kappa statistic to situations in which the categories are not equal in some respect - that is, weighted by an objective or subjective function.

Rank correlation is a method of finding the degree of association between two variables. The calculation for the rank correlation coefficient the same as that for the Pearson correlation coefficient, but is calculated using the ranks of the observations and not their numerical values. This…

In latent variable models, a manifest variable (or indicator) is an observable variable - i.e. a variable that can be measured directly. A manifest variable can be continuous or categorical. The opposite concept is the latent variable.

Fisher´s exact test is the first (historically) permutation test. It is used with two samples of binary data, and tests the null hypothesis that the two samples are drawn from populations with equal but unknown proportions of "successes" (e.g. proportion of patients recovered without complications…

Homoscedasticity generally means equal variation of data, e.g. equal variance.

Posterior probability is a revised probability that takes into account new available information. For example, let there be two urns, urn A having 5 black balls and 10 red balls and urn B having 10 black balls and 5 red balls. Now if an urn…

A loss function specifies a penalty for an incorrect estimate from a statistical model. Typical loss functions might specify the penalty as a function of the difference between the estimate and the true value, or simply as a binary value depending on whether the estimate…

Endogenous variables in causal modeling are the variables with causal links (arrows) leading to them from other variables in the model. In other words, endogenous variables have explicit causes within the model. The concept of endogenous variable is fundamental in path analysis and structural equation…

Causal modeling is aimed at advancing reasonable hypotheses about underlying causal relationships between the dependent and independent variables. Consider for example a simple linear model: y = a0 + a1 x1 + a2 x2 + e where y is the dependent variable, x1 and x2…

A time series x_t is called to be nonstationary if its statistical properties depend on time. The opposite concept is stationary time series . Most real world time series are nonstationary. An example of a nonstationary time series is a record of readings of the…

In an experiment, an arm is a treatment protocol - for example, drug A, or placebo.   In medical trials, an arm corresponds to a patient group receiving a specified therapy.  The term is also relevant for bandit algorithms for web testing, where an arm consists…

A sparse matrix typically refers to a very large matrix of variables (features) and records (cases) in which most cells are empty or 0-valued.  An example might be a binary matrix used to power web searches - columns representing search terms and rows representing searches,…

We continue our effort to shed light on potentially confusing usage of terms in the different data science communities. In statistics, a sample is a collection of observations or records.  It is often, but not always, randomly drawn.  In matrix form, the rows are records…

With this entry, we inaugurate a new effort to shed light on potentially confusing usage of terms in the different data science communities. In statistics and machine learning, normalization of variables means to subtract the mean and divide by the standard deviation.  When there are…

The Kolmogorov-Smirnov one-sample test is a goodness-of-fit test, and tests whether an observed dataset is consistent with an hypothesized theoretical distribution. The test involves specifying the cumulative frequency distribution which would occur given the theoretical distribution and comparing that with the observed cumulative frequency distribution.

Cohort data records multiple observations over time for a set of individuals or units tied together by some event (say, born in the same year). See also longitudinal data and panel data.

Six sigma means literally six standard deviations. The phrase refers to the limits drawn on statistical process control charts used to plot statistics from samples taken regularly from a production process. Consider the process mean. A process is deemed to be "in control" at any…

Psychometrics or psychological testing is concerned with quantification (measurement) of human characteristics, behavior, performance, health, etc., as well as with design and analysis of studies based on such measurements. An example of the problems being solved in psychometrics is the measurement of intelligence via "IQ"…

Azure is the Microsoft Cloud Computing Platform and Services.  ML stands for Machine Learning, and is one of the services.  Like other cloud computing services, you purchase it on a metered basis - as of 2015, there was a per-prediction charge, and a compute time…

Categorical variables are non-numeric "category" variables, e.g. color.  Ordered categorical variables are category variables that have a quantitative dimension that can be ordered but is not on a regular scale.  Doctors rate pain on a scale of 1 to 10 - a "2" has no…

Bimodal literally means "two modes" and is typically used to describe distributions of values that have two centers.  For example, the distribution of heights in a sample of adults might have two peaks, one for women and one for men.  

HDFS is the Hadoop Distributed File System.  It is designed to accommodate parallel processing on clusters of commodity hardware, and to be fault tolerant.

The Kruskal-Wallis test is a nonparametric test for finding if three or more independent samples come from populations having the same distribution. It is a nonparametric version of ANOVA.

A statistical technique which helps in making inference whether three or more samples might come from populations having the same mean; specifically, whether the differences among the samples might be caused by chance variation.

A two-tailed test is a hypothesis test in which the null hypothesis is rejected if the observed sample statistic is more extreme than the critical value in either direction (higher than the positive critical value or lower than the negative critical value). A two-tailed test…

In psychometric surveys, the split-halves method is used to measure the internal consistency reliability of survey instruments, e.g. psychological tests. The idea is to split the items (questions) related to the same construct to be measured, e.d. the anxiety level, and to compare the results…

In survival analysis, life tables summarize lifetime data or, generally speaking, time-to-event data. Rows in a life table usually correspond to time intervals, columns to the following categories: (i) not "failed", (ii) "failed", (iii) censored (withdrawn), and the sum of the three called "the number…

Truncation, generally speaking, means to shorten. In statistics it can mean the process of limiting consideration or analysis to data that meet certain criteria (for example, the patients still alive at a certain point). Or it can refer to a data distribution where values above…

This test is used for testing the significance of unplanned pairwise comparisons. When you do multiple significance tests, the chance of finding a "significant" difference just by chance increases. Tukey´s HSD test is one of several methods of ensuring that the chance of finding a…

A robust filter is a filter that is not sensitive to input noise values with extremely large magnitude (e.g. those arising due to anomalous measurement errors). The median filter is an example of a robust filter. Linear filters are not robust - their output may…

Hypothesis testing (also called "significance testing") is a statistical procedure for discriminating between two statistical hypotheses - the null hypothesis (H₀) and the alternative hypothesis ( H_a, often denoted as H₁). Hypothesis testing, in a formal logic sense, rests on the presumption of validity of the null hypothesis - that is, the null hypothesis is rejected only if the data at hand testify strongly enough against it.

Kurtosis measures the "heaviness of the tails" of a distribution (in compared to a normal distribution). Kurtosis is positive if the tails are "heavier" then for a normal distribution, and negative if the tails are "lighter" than for a normal distribution. The normal distribution has kurtosis of zero.

A "discovery" is a hypothesis test that yields a statistically significant result. The false discovery rate is the proportion of discoveries that are, in reality, not significant (a Type-I error). The true false discovery rate is not known, since the true state of nature is not known (if it were, there would be no need for statistical inference).

The signal is the component of the observed data that carries useful information.

Non-parametric regression methods are aimed at describing a relationship between the dependent and independent variables...

A nominal scale is really a list of categories to which objects can be classified.

The noise is the component of the observed data (e.g. of a time series) that is random and carries no useful information.

The single linkage clustering method (or the nearest neighbor method) is a method of calculating distance between clusters in hierarchical cluster analysis .

In a network analysis context, "edge" refers to a link or connection between two entities in a network

The 2006 Netflix Contest has come to convey the idea of crowdsourced predictive modeling, in which a dataset and a prediction challenge are made publicly available.  Individuals and teams then compete to develop the best performing model.

The linear model is ubiquitous in classical statistics, yet real-life data rarely follow a purely linear pattern.

Association rules, also called "market basket analysis," is a data mining method applied to transaction data.

This week's word is actually a letter.  R is a statistical computing and programming language and program, a derivative of the commercial S-PLUS program, which, in turn, was an offshoot of S from Bell Labs.

With the advent of Big Data and data mining, statistical methods like regression and CART have been repurposed to use as tools in predictive modeling.

The Netflix prize was a famous early application of crowdsourcing to predictive modeling.

An A-B test is a classic statistical design in which individuals or subjects are randomly split into two groups and some intervention or treatment is applied.

In time series forecasting, a moving average is a smoothing method in which the forecast for time t is the average value for the w periods ending with time t-1.

In regression models, an interaction term captures the joint effect of two variables that is not captured in the modeling of the two terms individually.

A naive forecast or prediction is one that is extremely simple and does not rely on a statistical model (or can be expressed as a very basic form of a model).

RMSE is root mean squared error.  In predicting a numerical outcome with a statistical model, predicted values rarely match actual outcomes exactly.

A label is a category into which a record falls, usually in the context of predictive modeling.  Label, class and category are different names for discrete values of a target (outcome) variable.

A strip transect is a small subsection of a geographically-defined study area, typically chosen randomly.

Spark is a second generation computing environment that sits on top of a Hadoop system, supporting the workflows that leverage a distributed file system.

Bandits refers to a class of algorithms in which users or subjects make repeated choices among, or decisions in reaction to, multiple alternatives.

In discrete response models, overdispersion occurs when there is more correlation in the data than is allowed by the assumptions that the model makes.

In a classification model, the confusion matrix shows the counts of correct and erroneous classifications.  In a binary classification problem, the matrix consists of 4 cells.

In a classic statistical experiment, treatment(s) and placebo are applied to randomly assigned subjects, and, at the end of the experiment, outcomes are compared.

Classification and regression trees, applied to data with known values for an outcome variable, derive models with rules like "If taxable income <$80,000, if no Schedule C income, if standard deduction taken, then no-audit."

The predictors in a predictive model are sometimes given different terms by different disciplines.  Traditional statisticians think in terms of variables.

In logistic regression, we seek to estimate the relationship between predictor variables Xi and a binary response variable.  Specifically, we want to estimate the probability p that the response variable will be a 0 or a 1.

Bayesian statistics typically incorporates new information (e.g. from a diagnostic test, or a recently drawn sample) to answer a question of the form "What is the probability that..."

Consider two (or more) samples subjected to different treatments.  A permutation test assesses whether,

One avid reader took issue with a recent definition of "quasi experiment."  I had defined it

In social science research, particularly in the qualitative literature on program evaluation, the term "quasi-experiment" refers to studies that do not involve the application of treatments via random assignment of subjects.

In survey research, curb-stoning refers to the deliberate fabrication of survey interview data by the interviewer.

Bag-of-words is a simplified natural language processing concept.

In language processing, stemming is the process of taking multiple forms of the same word and reducing them to the same basic core form.

Structured data is data that is in a form that can be used to develop statistical or machine learning models (typically a matrix where rows are records and columns are variables or features).

In predictive modeling, a key step is to turn available data (which may come from varied sources and be messy) into an orderly matrix of rows (records to be predicted) and columns (predictor variables or features).

A full Bayesian classifier is a supervised learning technique that assigns a class to a record by finding other records  with attributes just like it has, and finding the most prevalent class among them.

In computer science, MapReduce is a procedure that prepares data for parallel processing on multiple computers. 

Likert scales are categorical ordinal scales used in social sciences to measure attitude.  A typical example is a set of response options ranging from "strongly agree" to "strongly disagree."

A node is an entity in a network.  In a social network, it would be a person.  In a digital network, it would be a computer or device.

Latent variable models postulate some relationship between the statistical properties of observable variables.

K-nearest-neighbor (K-NN) is a machine learning predictive algorithm that relies on calculation of distances between pairs of records.

The kappa statistic measures the extent to which different raters or examiners differ when looking at the same data and assigning categories.

Censoring in time-series data occurs when some event causes subjects to cease producing data for reasons beyond the control of the investigator, or for reasons external to the issue being studied.

Survival analysis is a set of methods used to model and analyze survival data, also called time-to-event data.  

The probability distribution for X is the possible values of X and their associated probabilities. With two separate discrete random variables, X and Y, the joint probability distribution is the function f(x,y)