Methods terms

Choropleth map A map where regions are shaded or colored to represent the value of a statistic, such as population density. Cohort analysis Grouping customers by a shared starting point, such as signup month, and tracking how their behavior diverges over time. Cost of living index (COLI) An index comparing the relative cost of a fixed basket of goods and services across different places. Data storytelling Combining data, visuals and narrative so an audience understands an analysis and knows what to do about it. Data visualization Representing data graphically so that patterns, trends and outliers are easier to see than in a table of numbers. Data visualization literacy The ability to accurately read, interpret, and critically evaluate charts and graphs, and to recognize when one is misleading. Dependency ratio The ratio of dependents, children and older adults, to the working-age population, per 100 people. Descriptive analytics Analytics that summarizes what has already happened in the data, without explaining why or predicting what comes next. Fertility rate (total fertility rate, TFR) The average number of children a woman would have in her lifetime at current age-specific birth rates. Gini coefficient A single number, from 0 to 1, measuring income or wealth inequality within a population. House price index (HPI) An index tracking how home prices change over time using repeat sales of the same properties. Housing affordability index An index measuring whether a typical household can afford a typical home in a given housing market. Human Development Index (HDI) A composite index combining life expectancy, education, and income to rank and compare countries' overall development. Interquartile range The range covering the middle 50% of a dataset, from the 25th to the 75th percentile, used to measure spread. Life expectancy The average number of years a person could expect to live given current age-specific mortality rates. Median The middle value of a dataset when sorted from lowest to highest, splitting it into two equal halves. Median household income The income level at which half of households earn more and half earn less, in a given year and place. Moving average A calculated series that averages a fixed window of recent data points, smoothing out short-term noise to reveal a trend. Outlier An observation that differs markedly from the rest of the dataset, either from genuine variation or from an error. Percentile The value below which a given percentage of observations in a dataset fall. Population pyramid A chart showing a population's age and sex distribution, whose shape reveals growth, aging, or decline. Poverty headcount ratio The share of a population living below a defined poverty line, most often expressed as a percentage. Real-world evidence (RWE) Clinical evidence about how a treatment performs, generated from real-world data instead of a controlled trial. Rent burden The share of a renter household's income spent on rent, commonly flagged as burdened above 30 percent. RFM analysis A segmentation technique that scores customers on how recently, how often and how much they buy. Seasonality A regular, predictable pattern in data that repeats at a fixed calendar interval, such as daily, weekly, or annually. Standard deviation A measure of how spread out a set of values is around its mean, expressed in the same units as the data. Vacancy rate The share of housing units that are unoccupied and available, a key gauge of how tight a rental market is. Variance The average of the squared differences between each value and the mean, quantifying spread before returning to original units. Weighted average An average in which some values contribute more to the result than others, based on an assigned weight. Z-score The number of standard deviations a value lies above or below the mean of its distribution. Anomaly detection Identifying data points or patterns that deviate significantly from expected or normal behavior. Change-point detection Identifying the points in a time series where its underlying statistical behavior shifts abruptly rather than drifting gradually. Correlation A statistic between -1 and 1 that measures the strength and direction of a linear relationship between two variables. Diagnostic analytics Analytics focused on explaining why something happened by drilling into patterns, correlations, and root causes behind results. Pareto analysis A technique that ranks causes or categories by their contribution to a problem, to focus effort on the small number responsible for most of it. Root cause analysis (RCA) A structured process for tracing a problem back through its contributing factors to the underlying cause, not just its symptoms. Sampling bias A systematic distortion that arises when the method used to select a sample favors some members of the population over others. Selection bias A distortion that occurs when the sample analyzed is not representative of the population it is meant to describe. Simpson's paradox A pattern where a trend appears in several groups of data but reverses or disappears when the groups are combined. Spurious correlation A statistically strong relationship between two variables that has no causal or meaningful connection. Survivorship bias A distortion that occurs when analysis focuses only on entities that "survived" a filter, ignoring those that failed or dropped out. ARIMA (autoregressive integrated moving average) A classical time-series forecasting model that combines a series' own past values, differencing, and past forecast errors. AutoML (automated machine learning) Tooling that automates parts of building a machine learning model, such as feature selection, algorithm choice and hyperparameter tuning. Classification A supervised learning task that assigns each input to one of a fixed set of discrete categories or labels. Clustering An unsupervised technique that groups data points so items within a group are more similar to each other than to items outside it. Computer vision A field of AI that trains models to interpret and extract information from images and video, such as detecting objects or faces. Concept drift A change over time in the underlying relationship between a model's inputs and the outcome it predicts. Confusion matrix A table comparing a classifier's predicted labels against actual labels, breaking results into true and false positives and negatives. Cross-validation A method for estimating how well a model generalizes by repeatedly training and testing it on different splits of the same data. Decision tree A supervised model that predicts an outcome by following a sequence of if-then splits on feature values, shaped like a tree. Deep learning Machine learning using neural networks with many layers, able to learn complex patterns directly from raw data such as images or text. Demand forecasting Estimating future customer demand for a product so inventory, production and staffing can be planned ahead. Dimensionality reduction Techniques that compress a dataset with many variables into fewer dimensions while preserving as much meaningful information as possible. Displacement risk A modeled estimate of how likely a neighborhood or household is to experience housing-driven displacement. Elo rating A rating system that updates a competitor's skill estimate after each result, weighted by the strength of the opponent faced. Ensemble forecasting Running many slightly varied forecast simulations to express weather or climate uncertainty as a range instead of one number. Exponential smoothing A forecasting technique that weights recent observations more heavily than older ones, with the weight decaying exponentially with age. F1 score The harmonic mean of precision and recall, giving a single score that balances both when neither error type is clearly more costly. Feature engineering Creating, transforming or selecting the input variables a model is trained on, to make the underlying pattern easier for it to learn. Gradient boosting An ensemble method that builds trees one at a time, each one correcting the errors made by the trees before it. Hyperparameter tuning The process of finding the settings that control how a model learns, such as learning rate or tree depth, rather than what it learns. K-means clustering A clustering algorithm that partitions data into k groups by repeatedly assigning points to the nearest of k cluster centers. Likely voter model A statistical filter pollsters apply to a survey sample to estimate results among the people who will actually vote. Load forecasting Predicting future electricity demand so utilities can plan generation, purchasing, and grid operations. Logistic regression A statistical model that estimates the probability of a binary outcome as a function of input variables. Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Microtargeting Using granular voter or consumer data to reach narrowly defined audience segments with tailored messages. Monte Carlo simulation A technique that models uncertainty by running a scenario many times with randomly sampled inputs to see the range of outcomes. Neural network A model made of layers of connected nodes that learns to map inputs to outputs by adjusting connection weights during training. Overfitting When a model learns the noise and specific quirks of its training data so closely that it performs poorly on new data. Persuasion modeling Predicting which voters are movable on a candidate or issue, as distinct from how likely they are to vote. Population health management (PHM) Using aggregated patient data to improve health outcomes and control cost across a defined group of people, not just individuals. Precision and recall Two complementary classifier metrics; precision is how many predicted positives were correct, recall is how many actual positives were found. Predictive analytics Analytics that uses historical data and statistical models to estimate what is likely to happen next. Predictive maintenance Using sensor data and models to predict when equipment is likely to fail so it can be serviced before it breaks down. Predictive policing Using historical crime data and statistical models to forecast where or by whom future crime is likely, to guide patrols. Propensity model A predictive model that scores how likely an individual is to take a specific action, such as buying, churning, or clicking. Random forest An ensemble model that trains many decision trees on random subsets of data and features, then averages their predictions. Regression analysis A statistical method for estimating the relationship between one outcome variable and one or more predictor variables. ROC AUC (area under the ROC curve) A single number summarizing how well a classifier separates positive from negative cases across every possible decision threshold. Sales forecasting Predicting future sales volume or revenue from pipeline data, historical trends or statistical models. SHAP values (SHapley Additive exPlanations) A method for explaining an individual prediction by fairly attributing it to the contribution of each input feature. Small area estimation (SAE) Statistical techniques for producing reliable estimates for areas or subgroups too small to sample directly. Species distribution model (SDM) A statistical model that predicts where a species can occur by relating known sightings to environmental variables. Supervised learning A machine learning approach where a model learns from examples that already include the correct answer, to predict that answer on new data. Time series forecasting Predicting future values of a metric using patterns found in its own historical sequence over time. Topic modeling An unsupervised machine learning technique that discovers the latent themes running through a collection of documents. Train-test split Dividing a dataset into separate portions for training a model and for evaluating it, so performance is measured on unseen data. Training data The dataset with known outcomes that a machine learning model learns patterns from before being used to predict new cases. Turnout modeling Predicting the probability that an individual registered voter will cast a ballot in a given election. Unsupervised learning A machine learning approach that finds structure or patterns in data that has no labeled correct answer to learn from. Uplift modeling Predictive modeling that estimates how much a treatment changes an individual's outcome, to target only the people it will actually influence. Win probability model A statistical model estimating a team's real-time chance of winning a game given the current score, time remaining and situation. Decision intelligence A discipline combining data, analytics, and decision modeling to design, support, and improve how organizations make decisions. Dynamic pricing Automatically adjusting prices in near real time based on demand, inventory, timing, and competitor behavior. Linear programming (LP) An optimization method that finds the best outcome for a problem whose objective and constraints are all linear relationships. Markdown optimization Using data and models to decide when, how much, and on which products to cut price so excess inventory clears at the best return. Multi-armed bandit (MAB) An adaptive experiment that shifts traffic toward better-performing variants in real time, instead of splitting it evenly until a fixed end date. Next-best-action (NBA) A system or process that recommends the single most valuable action to take for a specific customer at a specific moment. Optimization modeling Using mathematical models to find the best possible decision under a defined set of constraints and an objective to optimize. Prescriptive analytics Analytics that recommends specific actions by combining predictions with optimization or decision rules. Recommendation system A system that predicts and ranks the items a specific user is most likely to want, such as products, content, or actions. Reinforcement learning A learning approach where an agent learns a strategy by taking actions in an environment and receiving rewards or penalties over time. Scenario planning A planning method that builds and stress-tests a small number of distinct, plausible futures rather than a single forecast. What-if analysis Testing how a model's output changes when one or more input assumptions are changed, to explore specific alternative outcomes. A/A test An experiment that splits users into two identical groups with no real difference, used to check that the testing system itself is unbiased. A/B testing A controlled experiment that compares two variants to measure which one produces a better outcome. Average treatment effect (ATE) The mean difference in outcome between treated and untreated units across a full population, the standard headline result of an experiment. Causal inference The set of methods used to determine whether one variable actually causes a change in another, not merely correlates with it. Clinical trial analytics Analytics applied to designing, running, and analyzing clinical trials, from site selection through final results. Conversion lift study A randomized test comparing a holdout who saw no ads against an exposed group, to measure advertising's true incremental effect on conversions. Counterfactual What would have happened to a unit or group without a treatment, the unobservable baseline every causal claim is measured against. CUPED (controlled-experiment using pre-experiment data) A variance-reduction technique that uses each user's pre-experiment behavior to sharpen an experiment's estimate without adding more traffic. Difference-in-differences (DiD) A causal method that compares the before-and-after change in a treated group against the before-and-after change in an untreated group. Effect size A standardized measure of the magnitude of a difference or relationship, independent of sample size. False discovery rate (FDR) The expected share of "significant" results that are actually false positives, when many statistical tests are run and checked at once. Feature flag A configuration switch that turns a piece of functionality on or off, or for specific users, without deploying new code. Geo experiment An experiment that randomizes treatment by geographic region rather than by individual user, often to measure marketing's incremental impact. Guardrail metric A secondary metric watched during an experiment to catch harm the primary metric would not show, such as load time or churn. Heterogeneous treatment effects (HTE) Differences in a treatment's true effect across subgroups, showing an intervention can help some segments and harm or not affect others. Holdout group A segment of users deliberately kept out of a treatment, campaign or feature, so its true incremental impact can be measured against them. Impact evaluation Measuring a program's causal effect on outcomes by comparing what happened to a credible counterfactual. Incrementality testing An experiment measuring the true causal lift of a marketing activity by comparing it against a held-out control. Instrumental variables (IV) A causal-inference technique that uses a variable affecting the outcome only through treatment, to work around unmeasured confounding. Marketing mix modeling A statistical technique estimating how much each marketing channel and external factor contributes to results. Minimum detectable effect (MDE) The smallest true effect an experiment is designed to reliably distinguish from no effect, given its sample size and power. Multiple comparisons problem The increased risk of a false positive that comes from running many statistical tests and highlighting any one significant result. Multivariate testing (MVT) An experiment that varies several page or product elements at once, to measure each element's individual effect and how elements interact. Novelty effect A temporary spike in engagement with a new feature or design that fades once users stop reacting to its newness and it becomes routine. Overall evaluation criterion (OEC) The single metric or composite formula a team agrees in advance will determine whether an experiment's treatment is judged a success. Preregistration Publicly documenting a study's hypotheses, methods and analysis plan before collecting or examining the data. Program evaluation Systematic assessment of whether a public or nonprofit program achieves its intended outcomes, and why. Propensity score matching (PSM) A method that pairs treated and untreated units with similar odds of having received treatment, to approximate a randomized comparison. Randomized controlled trial (RCT) An experiment that randomly assigns subjects to treatment and control groups to isolate a causal effect. Regression discontinuity design (RDD) A causal method that compares outcomes just above and below a cutoff, such as a score threshold, that determines who receives a treatment. Sample ratio mismatch (SRM) A significant gap between an experiment's actual traffic split and its intended split, signaling a bug rather than a real treatment effect. Sample size The number of observations in a study or test, which determines how precisely and reliably an effect can be measured. Sequential testing An analysis method that lets experiments be checked continuously without inflating the false-positive rate from repeated peeking. Split URL testing An A/B test that sends visitors to entirely separate page URLs for each variant, instead of changing content dynamically on one shared URL. Statistical power The probability that a hypothesis test correctly detects a real effect, given that one truly exists. Statistical significance A threshold judgment that an observed effect is unlikely to be due to chance alone, given the data and test used. Switchback experiment An experiment design that alternates a whole system between treatment and control over time, for cases where individual users can't be split. Synthetic control A method that builds a weighted composite of untreated units as the counterfactual for a single treated unit, such as one market or region. T-test A statistical test that compares the means of one or two groups to judge whether an observed difference is likely real. Autocorrelation The correlation of a time series with a lagged, delayed copy of itself, used to detect repeating patterns and dependence over time. Bayesian inference A statistical approach that updates the probability of a belief as new evidence arrives, starting from a prior assumption. Bootstrapping A technique for estimating the uncertainty of a statistic by repeatedly resampling the observed data with replacement. Central limit theorem The principle that the average of many independent samples tends toward a normal distribution, regardless of the data's shape. Chi-square test A statistical test that checks whether observed frequencies in categorical data differ from what chance would produce. Confidence interval A range calculated from sample data that, under repeated sampling, would contain the true value a stated percentage of the time. Design of experiments (DOE) A structured, statistical approach to planning experiments so the effect of each factor can be estimated efficiently. Differential privacy A mathematical technique for publishing aggregate statistics while guaranteeing no individual record can be inferred from the results. Expected value (EV) The probability-weighted average outcome of a bet or decision, used to judge whether it is favorable over the long run. Exploratory data analysis An open-ended first pass through a dataset to understand its structure and spot patterns before formal analysis. Hypothesis testing A statistical framework for deciding whether observed data provides enough evidence to reject a default assumption. Likert scale An ordinal rating scale, typically five or seven points, running between two opposite attitudes or opinions. Margin of error The range added to and subtracted from a survey estimate to express the uncertainty from sampling only part of a population. Multilevel regression and poststratification (MRP) A method for estimating opinion in small geographic or demographic groups from a national survey by modeling and reweighting. Nonresponse bias Distortion in survey results that occurs when people who decline to participate differ systematically from those who respond. Normal distribution A symmetric, bell-shaped probability distribution defined entirely by its mean and standard deviation. Null hypothesis The default assumption in a statistical test, typically that there is no effect or no difference, until evidence says otherwise. P-value The probability of observing data at least as extreme as the result, if the null hypothesis were actually true. Questionnaire design Writing and structuring survey questions and response options to minimize bias and measure reliably. Record linkage Matching records that refer to the same person or entity across datasets that lack a shared unique identifier. Regression to the mean (in sports) The statistical tendency for unusually good or bad performances to move back toward a player's or team's underlying average. Response rate The share of people selected for a survey who actually complete it, a key indicator of potential nonresponse bias. Sampling frame The list or source, such as a voter file or address database, from which a survey's respondents are actually drawn. Sampling in analytics Computing a report from a subset of the underlying data rather than the complete data set, to return results faster. Standard error The estimated variability of a sample statistic, such as the mean, if the study were repeated across new samples. Statistical process control (SPC) Using control charts and statistical limits to tell normal process variation apart from a genuine problem in real time. Survey mode effects Systematic differences in how people answer identical questions depending on the survey's mode of delivery. Survey weighting Adjusting survey responses so the sample's demographic makeup matches the target population's known composition. Type I and Type II errors The two ways a hypothesis test can be wrong, rejecting a true null, or failing to reject a false one. Agentic analytics The use of AI agents that autonomously plan and execute multi-step analytics work, such as investigating a metric change. AI agent A system that uses a language model to plan and take multi-step actions, such as calling tools or APIs, toward a goal. AI copilot An AI tool embedded in a work application that assists a human with suggestions or drafts, rather than working unsupervised. Augmented analytics Using AI and machine learning to automate parts of data preparation, insight discovery, and explanation in analytics tools. Generative AI AI systems that create new content, text, images, audio, or code, rather than only classifying or predicting a label. Natural language query Asking a data question in plain language and getting an answer without writing code or a query language. Text-to-SQL Technology that translates a plain-language question directly into a runnable SQL query against a database. Embeddings Numeric vector representations of text, images, or other data, positioned so similar items sit close together in the vector space. Named Entity Recognition (NER) An NLP technique that locates and classifies names of people, organizations, places, and other entities within text. Net sentiment The share of positive mentions minus the share of negative mentions in a set of analyzed text, summarized as one score. Sentiment analysis Using NLP to classify text as positive, negative, or neutral, so large volumes of comments or reviews can be scored automatically. Text analytics The techniques used to extract structured information and insight from unstructured text, from keyword counts to deep NLP. AIS vessel tracking Using shipboard AIS radio broadcasts to track vessel position, speed and identity for maritime and environmental analysis. Cell site analytics Analyzing performance and usage data from individual cell towers to manage network capacity, coverage and quality. Crime mapping Plotting reported crime incidents geographically to reveal spatial patterns and support resource deployment decisions. Deforestation monitoring Tracking loss of tree cover over time, usually from satellite imagery, to detect and quantify forest clearing. Footfall analytics Measuring and analyzing how many people enter, move through, and dwell in a physical space, most often a retail store. Geocoding Converting a text address or place name into geographic coordinates, or the reverse, for mapping and analysis. Geofencing Drawing a virtual boundary around a location so entry or exit can trigger an action or be logged as an event. Geographic information system (GIS) Software for capturing, storing, analyzing and displaying data tied to specific locations on the Earth's surface. Geospatial intelligence (GEOINT) Intelligence built from imagery, mapping, and geographic data to describe and assess activity tied to a place. Historical GIS Applying geographic information systems to historical data to map how places, boundaries and populations changed over time. Hotspot analysis Statistical method for finding geographic clusters where an event, such as crime or disease, occurs more than chance predicts. Normalized difference vegetation index (NDVI) A satellite-derived index measuring live vegetation density and health from how plants reflect red and infrared light. Ocean color remote sensing Measuring the color of reflected sunlight from the ocean surface by satellite to estimate chlorophyll, sediment and water quality. Pattern-of-life analysis Studying the routine movements and behavior of a person, vehicle, or location over time to detect meaningful deviations. Precision agriculture Using field-level data, from sensors, satellites, and GPS-guided equipment, to tailor farming inputs to specific conditions rather than whole fields. Remote sensing Collecting data about the Earth's surface or atmosphere from satellites, aircraft or drones without physical contact. Satellite imagery analysis Extracting information such as land cover, change or object detection from images captured by orbiting satellites. Spatial analysis Analytical techniques that use the location, distance or arrangement of data to find patterns tabular data hides. Centrality measures A family of graph metrics that rank how important or well-connected each node is within a network. Community detection Identifying groups of nodes in a network that are more densely connected to each other than to the rest of the graph. Graph analytics Analyzing data modeled as nodes and edges to study connections, structure, and influence rather than isolated records. Link prediction Estimating the likelihood that a connection exists, or will form, between two nodes in a graph that are not currently linked. PageRank An algorithm that scores a node's importance by the number and importance of other nodes linking to it. Video analytics Extracting quantitative insight from video, whether audience behavior around published video or automated analysis of video frames. Watch time The total minutes viewers spend watching a piece of video content, a key ranking and monetization signal on video platforms. Audio analytics The analysis of sound and audio signals, from speech and music to ambient noise, to extract patterns and meaning. Automatic speech recognition (ASR) Technology that converts spoken audio into written text, forming the input layer for most speech and voice analytics. Speaker diarization Splitting an audio recording into segments and labeling which speaker was talking in each, without necessarily identifying who they are. Speech analytics Software that transcribes and analyzes spoken audio, such as call center recordings, to extract sentiment, keywords, and compliance signals. Word error rate (WER) The standard accuracy metric for speech recognition, measuring the edit distance between a transcript and the correct reference text. Numerical weather prediction (NWP) Forecasting weather by solving the physical equations governing the atmosphere on a computer, using current observations as a start. Signal processing Techniques for analyzing, filtering, and transforming time-varying signals, such as sensor readings or audio, to extract or clean up information. Stationarity The property of a time series whose statistical characteristics, such as mean and variance, stay constant over time. Apache Kafka An open-source distributed event streaming platform that stores records in ordered, durable, replayable logs called topics. Event streaming Continuously publishing and consuming records of things that happened, in order, through a durable, replayable log. Event-driven architecture (EDA) A system design where components communicate by producing and reacting to events, rather than calling each other directly. Exactly-once processing A processing guarantee that each event affects the result exactly once, even after retries or redelivery. Late-arriving data Records that reach a pipeline after the time window they logically belong to has already been processed. Message broker Middleware that routes messages between producing and consuming systems so they don't need to connect to each other directly. Micro-batching Processing data in very small, frequent batches to approximate streaming without a dedicated stream processor. Stream processing Processing data continuously as individual events arrive, rather than waiting to accumulate a batch. Watermarking A stream processing technique estimating how far event time has progressed, to decide when a window can be finalized. Advanced metering infrastructure (AMI) Smart meters and the communication network that lets utilities collect near real-time usage data from them. Digital twin A live virtual model of a physical asset or process, kept synchronized with real sensor data, used to simulate and predict behavior. Fleet telematics Collecting real-time vehicle data, location, speed, fuel use, and diagnostics, from a fleet's onboard systems and sensors. Sensor calibration Adjusting a sensor's readings against a known reference so its measurements are accurate, not just precise. Smart city sensor data Real-time readings from networked devices, such as traffic counters and air-quality monitors, across urban infrastructure. Clickstream The raw, timestamped sequence of pages, clicks, and interactions a single visitor generates while browsing a site or app. Event data model The structured format — event name, timestamp, user ID, and properties — that a single tracked user action is recorded in. Sessionization Grouping a visitor's raw, timestamped events into discrete sessions, usually by closing a session after a period of inactivity. User journey analytics Analyzing the ordered sequence of touchpoints and actions a user takes across sessions and channels toward a goal. Apache Spark An open-source distributed computing engine for processing large datasets in parallel across a cluster of machines. Batch processing Processing data in large, scheduled groups rather than continuously as each record arrives. Data deduplication Identifying and removing records that represent the same real-world entity or event more than once. DataFrame A two-dimensional, table-like data structure with named, typed columns, used to manipulate data in code. Distributed computing Splitting a computation across multiple machines that coordinate over a network to solve it faster or at larger scale.