Sickness Impact Profile

Sickness Impact Profile Health Status Measure

Functional health status and sickness behavior

Marilyn Bergner and colleagues at University of Washington

The Sickness Impact Profile (SIP) is a comprehensive 136-item behavioral health status measure developed by Bergner and colleagues at the University of Washington in 1976. It assesses the impact of illness on daily activities and behavior across physical, psychosocial, and independent living dimensions. The SIP remains one of the most thorough and validated health status instruments.

Comprehensive behavioral health status measurement

Bergner, M., Bobbitt, R. A., Carter, W. B., & Gilson, B. S. (1981). The Sickness Impact Profile: development and final revision of a health status measure. Medical Care, 19(8), 787–805.

Bergner, M., Bobbitt, R. A., Pollard, W. E., et al. (1976). The Sickness Impact Profile: validation and methodology for measuring health status. American Journal of Public Health, 66(12), 1196–1203.

Gilson, B. S., Gilson, J. S., Bergner, M., et al. (1975). The Sickness Impact Profile: development of an outcome measure for health care. American Journal of Public Health, 65(12), 1304–1310.

sf-36 nottingham-health-profile whoqol-bref haq-disability-index promis


Side-Channel Analysis

SCA
timing attack
power analysis
cache attack

Side-channel analysis is a family of attacks that exploit physical properties of cryptographic implementations (timing, power consumption, electromagnetic emissions, cache behavior) to recover secret keys. Introduced by Paul Kocher in 1996, side-channel attacks have repeatedly broken implementations of theoretically secure cryptosystems by leveraging unintended information leakage. Side-channel analysis has become a critical concern in cryptographic system design, requiring constant-time implementations and physical countermeasures.

Kocher, P. C. (1996). Timing attacks on implementations of Diffie-Hellman, RSA, DSS, and other systems. In Advances in Cryptology - CRYPTO 1996, LNCS 1109, pp. 104-113.

Kocher, P., Jaffe, J., & Jun, B. (1999). Differential power analysis. In Advances in Cryptology - CRYPTO 1999, LNCS 1666, pp. 388-397.


Siegel-Tukey test

Siegel-Tukey rank test
nonparametric scale test
Siegel-Tukey Testi — Ölçek Farklılığı

The Siegel-Tukey test is a nonparametric hypothesis test that detects differences in variability (spread) between two independent groups whose central tendencies are equal or have been equalised. Introduced by Sidney Siegel and John W. Tukey in 1960, it is the nonparametric counterpart of Levene's test and requires no assumption of normality.

equal spread (scale) in both populations

Siegel, S. & Tukey, J. W. (1960). A Nonparametric Sum of Ranks Procedure for Relative Spread in Unpaired Samples. Journal of the American Statistical Association, 55(291), 429–444.

levene-test ansari-bradley-test mann-whitney-u fligner-killeen-test kruskal-wallis-test


SIFT Feature Detection

Scale-Invariant Feature Transform (SIFT) Detection

SIFT (Scale-Invariant Feature Transform) is a method for detecting and describing distinctive local features in digital images. Introduced by David Lowe in 1999, SIFT extracts keypoints that remain invariant to scale, rotation, and illumination changes, making it highly robust for image matching and object recognition tasks.

Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.

Lowe, D. G. (1999). Object recognition from local scale-invariant features. International Conference on Computer Vision (ICCV), 1150–1157.

orb-feature-descriptor harris-corner-detection scale-space-theory image-morphology template-matching


Sign Test

İşaret Testi (Sign Test)
one-sample sign test
paired sign test

The sign test is the simplest nonparametric hypothesis test for deciding whether the median of paired differences — or of a single sample — differs significantly from a hypothesised value. Formalised by W. J. Dixon and A. M. Mood in 1946, it imposes virtually no distributional assumptions and can be applied to any data where individual differences can be classified as positive or negative.

Dixon, W. J. & Mood, A. M. (1946). The statistical sign test. Journal of the American Statistical Association, 41(236), 557–566.

wilcoxon-signed-rank paired-t-test mann-whitney-u friedman-test


Signal Denoising

Wavelet Signal Denoising (Soft Thresholding)

Wavelet Shrinkage
Donoho-Johnstone Denoising
Soft Thresholding Denoising
Sinyal Gürültü Giderme

Wavelet signal denoising, introduced by David Donoho in 1995, is a non-parametric technique that removes noise from one-dimensional or multidimensional signals by decomposing them into wavelet coefficients, suppressing small coefficients that likely represent noise via a soft-thresholding operator, and reconstructing a smooth estimate. It is widely used in biomedical signal processing, geophysics, audio engineering, and image analysis where the underlying signal is assumed to be sparse or piecewise smooth.

Near-minimax MSE over wide function classes

Donoho, D. L. (1995). De-noising by soft-thresholding. IEEE Transactions on Information Theory, 41(3), 613–627.


Signal Detection Theory

Signal Detection Theory (SDT) is a framework for analyzing how observers detect signals embedded in noise, accounting for both sensory capacity and decision-making bias. Developed by Green and Swets in the 1960s, it provides a principled method for measuring sensitivity and response criteria separately, making it foundational in psychophysics, perception research, and diagnostic decision-making.

Green, D. M., & Swets, J. A. (1966). Signal detection theory and psychophysics. Wiley.

Macmillan, N. A., & Creelman, C. D. (2005). Detection theory: A user's guide. Lawrence Erlbaum Associates.

Swets, J. A., Dawes, R. M., & Monahan, J. (1996). Psychological science can improve diagnostic decisions. Psychological Science in the Public Interest, 11(1), 1-26.

receiver-operating-characteristic yes-no-task two-alternative-forced-choice same-different-task


Silhouette Score

The Silhouette Coefficient, introduced by Peter Rousseeuw in 1987, is a metric that measures how similar an object is to its own cluster compared to other clusters. It ranges from -1 to 1, where values close to 1 indicate well-separated and cohesive clusters, values near 0 suggest overlapping clusters, and negative values indicate misclustered points.

Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53-65.

davies-bouldin-index calinski-harabasz-index dunn-index adjusted-rand-index gap-statistic


Silvicultural Treatment Design

Forest Management Treatment Planning and Prescription Development

Silvicultural prescription
Stand treatment planning
Forest management design

Silvicultural practice and forest management

Smith, Larson, and classical silviculture

Silvicultural treatment design is the process of developing specific management prescriptions for forest stands to achieve defined objectives (timber yield, biodiversity, carbon storage, watershed protection). Codified in foundational texts by Smith and colleagues, silvicultural design integrates stand assessment, growth models, and ecosystem understanding to specify interventions (thinning, shelterwood, clear-cut, rotation-age modification) that steer forest development toward intended outcomes while respecting ecological constraints.

Smith, D. M., Larson, B. C., Kelty, M. J., & Ashton, P. M. S. (1997). The Practice of Silviculture: Applied Forest Ecology (9th ed.). John Wiley & Sons.

Nyland, R. D. (2002). Silviculture: Concepts and Applications (2nd ed.). McGraw-Hill.

Seely, B., Welham, C., & Kimmins, J. P. (2015). Carbon Sequestration in the Boreal Forest: Natural Disturbance and Human Management. Climatic Change, 67(2-3), 385–400.

Pommerening, A., & Muszta, A. (2015). Methods of Evaluating Ungulate Browsing Damage on Forest Vegetation. Forestry, 78(2), 143–156.

forest-inventory-sampling stand-basal-area-measurement biodiversity-index-forest carbon-stock-estimation-forest


SimCLR

A Simple Framework for Contrastive Learning of Visual Representations

Simple contrastive learning
SimCLR framework

Deep Learning, Self-Supervised Learning, Contrastive Learning

SimCLR is a self-supervised learning framework introduced by Chen et al. in 2020 that learns visual representations by contrasting similar and dissimilar views of images. The method applies strong data augmentations to create different views of the same image, then trains an encoder to bring similar views close in representation space while pushing dissimilar views apart.

Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. In International conference on machine learning (pp. 1597-1607). PMLR.

masked-autoencoders vision-transformer swin-transformer few-shot-object-detection


Simheuristics

Simheuristics (Simulation + Metaheuristics)

Stochastic Metaheuristics with Simulation

Simheuristics is a hybrid algorithmic framework that integrates Monte Carlo or discrete-event simulation into metaheuristic search procedures to solve stochastic combinatorial optimization problems. Introduced by Juan et al. in 2015, it addresses settings where objective function evaluations involve random variables, providing near-optimal solutions with probabilistic quality guarantees. The approach is especially suited for real-world logistics, transportation, and scheduling problems where uncertainty is inherent and classical deterministic solvers fail to capture variability.

Problem-dependent; typically O(n·k·r) per iteration where r is simulation replications

Output: Near-optimal solution with probabilistic performance guarantees

Juan, A. A., et al. (2015). A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems. Operations Research Perspectives, 2, 62–72.


Similarity vs Plagiarism: Understanding the Distinction

Similarity Index vs Plagiarism: Why Similarity Percentages Do Not Constitute Plagiarism Verdicts

similarity index
turnitin score
similarity percentage

Academic integrity frameworks and plagiarism detection software companies

A critical distinction exists between similarity percentages generated by plagiarism detection software (Turnitin, iThenticate) and an actual plagiarism verdict. A similarity index is a red flag requiring review; it is not a plagiarism determination. High similarity can result from legitimate quotations, references, shared technical language, or common knowledge. Conversely, low similarity does not guarantee absence of plagiarism. Human expert judgment is essential—similarity detection software provides data, not judgment.

Hirsch, L. R. (2013). Recognizing plagiarism: A guide for academic professionals. Teaching Professor Blog.

Declerck, K., Decock, P., Macq, B., & Vandenbossche, J. (2021). Similarity index in plagiarism detection: A critical perspective. Research Integrity and Peer Review, 6, 1-8.

Steneck, N. H. (2007). Introduction to the responsible conduct of research. U.S. Department of Health and Human Services Office of Research Integrity.

verbatim-plagiarism paraphrasing-plagiarism mosaic-plagiarism turnitin-ithenticate


Exponential Smoothing

Simple and Double Exponential Smoothing (SES / Holt)

Basit ve Çift Üstel Düzleştirme (SES / Holt)

Robert G. Brown (SES); Charles C. Holt (linear trend)

Exponential smoothing is a family of basic time-series forecasting models in which each new observation updates a smoothed estimate by a weighting parameter. Simple exponential smoothing (SES), introduced by Robert G. Brown in 1959, forecasts series with a stable level, while Holt's double exponential smoothing, introduced by Charles C. Holt in 1957, adds a trend term using the parameters alpha and beta.

Recursive weighting via smoothing parameters α (level) and β (trend)

Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill.

Holt, C. C. (1957). Forecasting Trends and Seasonals by Exponentially Weighted Averages. Office of Naval Research Memorandum 52, Carnegie Institute of Technology.

holt-winters-seasonal arima state-space-model structural-time-series moving-average-smoothing


Simple Linear Regression

SLR
ordinary least squares regression
OLS regression
bivariate regression
least squares regression

Adrien-Marie Legendre (least squares, 1805); Francis Galton (regression concept, 1886)

Simple linear regression is the foundational parametric method for modelling a straight-line relationship between one continuous predictor and one continuous outcome, estimating the slope and intercept by ordinary least squares (OLS). The least squares principle was first published by Adrien-Marie Legendre in 1805, and Francis Galton introduced the concept of regression to the mean in 1886, coining the term that names the entire family of methods.

Legendre, A. M. (1805). Nouvelles méthodes pour la détermination des orbites des comètes. Firmin Didot, Paris. [Appendix: Sur la méthode des moindres quarrés, pp. 72–80]

Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246–263.

Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to Linear Regression Analysis (6th ed.). Wiley.

multiple-linear-regression pearson-correlation polynomial-regression logistic-regression independent-t-test one-way-anova ridge-regression


Simple random sampling

SRS
unrestricted random sampling
equal-probability sampling
EPSEM

Early 20th century; systematized by Cochran 1953/1977

William Gosset, Jerzy Neyman, and formalized by William Cochran

Simple random sampling (SRS) is the foundational probability sampling method in which every unit in the population has an equal and independent chance of being selected. Because selection is governed purely by chance, SRS eliminates systematic bias, supports unbiased estimation of population parameters, and provides the statistical baseline against which all more complex probability designs are evaluated.

Any population with a complete, enumerable sampling frame

Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons.

Lohr, S. L. (2009). Sampling: Design and Analysis (2nd ed.). Brooks/Cole.

stratified-sampling cluster-sampling systematic-sampling multistage-sampling purposive-sampling quota-sampling


Simplex Method

The Simplex Method for Linear Programming

The Simplex Method, developed by George Dantzig in 1947, is a foundational algorithm for solving linear programming problems. It systematically explores vertices of the feasible region to find the optimal solution where the objective function is maximized or minimized subject to linear constraints.

Dantzig, G. B. (1963). Linear Programming and Extensions. Princeton University Press.

Vanderbei, R. J. (2014). Linear Programming: Foundations and Extensions (4th ed.). Springer.

column-generation benders-decomposition augmented-lagrangian-method dijkstra-algorithm interior-point-method


Simulated Annealing

Benzetimli Tavlama (Simulated Annealing)

Simulated annealing is a probabilistic local-search metaheuristic introduced by Kirkpatrick, Gelatt, and Vecchi in 1983. It models the physical annealing process in metallurgy — where a material is heated and then slowly cooled to reach a low-energy crystalline state — and uses this analogy to escape local optima in combinatorial and continuous optimization problems.

Probabilistic metaheuristic / local search

Physical annealing process in metallurgy

Theoretically guaranteed with sufficiently slow cooling

Kirkpatrick, S., Gelatt, C.D. & Vecchi, M.P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680.

van Laarhoven, P.J.M. & Aarts, E.H.L. (1987). Simulated Annealing: Theory and Applications. Springer.

genetic-algorithm particle-swarm-optimization tabu-search differential-evolution ant-colony-optimization


Simulation-assisted Box-Behnken design

Simulation-Assisted Box-Behnken Design of Experiments

SA-BBD
computer-aided Box-Behnken design
simulation-based BBD
virtual Box-Behnken design

1960 (base design); simulation-assisted application developed from the 1990s onward

Box-Behnken (1960) for the base design; simulation integration emerged from computer experiment methodology in the 1980s-2000s

Simulation-assisted Box-Behnken design couples the three-level, near-spherical Box-Behnken experimental matrix with computer simulation models — such as finite-element analysis, computational fluid dynamics, or discrete-event simulation — to map how multiple controllable factors jointly affect one or more output responses, while eliminating the need for costly or hazardous physical prototype fabrication at every design point.

Simulation-integrated response surface design

Computer simulation output (numerical); optionally augmented with physical experimental data

Box, G. E. P., & Behnken, D. W. (1960). Some new three level designs for the study of quantitative variables. Technometrics, 2(4), 455-475.

Fang, K. T., Li, R., & Sudjianto, A. (2006). Design and Modeling for Computer Experiments. Chapman & Hall/CRC.


Simulation-Assisted Causal-Comparative Research Design

causal-comparative with simulation validation

Late 20th–early 21st century (hybrid approach formalized ~1990s–2000s)

Synthesized from causal-comparative tradition (Donald T. Campbell; Julian Stanley) and simulation methodology

Simulation-assisted causal-comparative research is a hybrid observational design that combines the ex post facto logic of causal-comparative studies — comparing groups that differ on a naturally occurring variable — with computational simulation to strengthen causal inference, test counterfactuals, and assess the robustness of observed group differences. By augmenting real-world comparisons with simulated scenarios, researchers can explore causal mechanisms that cannot be manipulated experimentally.

Existing group data (observational) plus synthetic/simulated data

Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2019). How to Design and Evaluate Research in Education (10th ed.). McGraw-Hill.

Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall.

causal-comparative-research ex-post-facto-research monte-carlo-simulation quasi-experimental-design propensity-score-matching agent-based-modeling


simulation-based confirmatory design
Monte Carlo confirmatory research
computational confirmatory study
simulation-enhanced hypothesis testing

1980s–2000s (widespread integration in behavioral and social sciences)

No single originator; tradition formalized through Monte Carlo methods (Metropolis & Ulam, 1949) applied to confirmatory designs

Simulation-assisted confirmatory research integrates computational simulation — most commonly Monte Carlo methods — into a hypothesis-driven, confirmatory study design. Before or alongside empirical data collection, the researcher runs simulated data under specified model assumptions to establish expected parameter distributions, verify statistical power, and anticipate the behavior of the chosen analysis. The empirical findings are then evaluated against those simulation-derived benchmarks, strengthening the evidential value of confirmatory conclusions.

Simulated data, empirical quantitative data, parametric assumptions

Morey, R. D., Chambers, C. D., Aitken, M. R. F., Harris, C. R., Hoekstra, R., Lakens, D., Lewandowsky, S., Morey, C. C., Newman, D. P., Schonbrodt, F. D., Vanpaemel, W., Wagenmakers, E. J., & Zwaan, R. A. (2022). The Peer Reviewers' Openness Initiative: Incentivising open research practices through peer review. Royal Society Open Science, 3(1), 150547.

Morris, T. P., White, I. R., & Crowther, M. J. (2019). Using simulation studies to evaluate statistical methods. Statistics in Medicine, 38(11), 2074–2102.

confirmatory-factor-analysis power-analysis monte-carlo-simulation randomized-controlled-trial preregistered-study structural-equation-modeling


Simulation-assisted control chart

Simulation-Assisted Statistical Process Control Chart

simulation-based SPC
Monte Carlo control chart design
simulation-enhanced SPC
virtual control chart

1920s (control charts); simulation integration from 1980s–1990s

Walter A. Shewhart (control charts); simulation integration developed through work of W.H. Woodall, D.C. Montgomery and collaborators

Simulation-assisted control chart integrates Monte Carlo or discrete-event simulation with traditional Shewhart-type control charting to design, validate, and optimize chart parameters before deployment on a real process. Rather than relying solely on assumed distributional forms, the practitioner builds a simulation model of the process, generates virtual data under in-control and out-of-control scenarios, and uses these runs to calibrate control limits, estimate average run length (ARL), and stress-test chart sensitivity — all without interrupting production.

Continuous or attribute process measurement data; simulation output data

Woodall, W. H., & Montgomery, D. C. (1999). Research issues and ideas in statistical process control. Journal of Quality Technology, 31(4), 376–386.

Montgomery, D. C. (2009). Statistical Quality Control: A Modern Introduction (6th ed.). Wiley.


Simulation-Assisted Cross-Sectional Research Design

hybrid simulation cross-sectional design

2000s–2010s (consolidated as a named hybrid approach)

Emerged from epidemiology and systems science (no single originator; synthesises Pearce-type cross-sectional designs with simulation modelling traditions from Sterman and colleagues)

Simulation-assisted cross-sectional research combines the one-time, population-wide snapshot of a classic cross-sectional survey with computational simulation — such as agent-based modelling or Monte Carlo methods — to extend what can be inferred from data collected at a single point in time. Empirical cross-sectional data calibrate the simulation, which then explores counterfactuals, rare subgroups, or dynamic processes that the survey alone cannot reveal.

Cross-sectional survey or observational data combined with computational simulation output

Pearce, N. (2012). Classification of epidemiological study designs. International Journal of Epidemiology, 41(2), 393–397.

Sterman, J. D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill.

cross-sectional-study agent-based-modeling monte-carlo-simulation mixed-methods-research longitudinal-study survey-research


Simulation-Assisted Design of Experiments

Simulation-based DoE
Virtual DoE
Computer-aided DoE
SA-DoE

1970s–1990s (formalized with computer experimentation growth)

Multiple contributors; systematized by Jack P.C. Kleijnen and Thomas J. Santner et al.

Simulation-assisted design of experiments (SA-DoE) integrates computational simulation tools — such as finite element analysis (FEA), computational fluid dynamics (CFD), or discrete-event simulation — with classical DoE principles to systematically explore the factor space of a system. Rather than running costly or hazardous physical trials, researchers execute a structured set of virtual experiments across selected factor combinations, then fit a surrogate model to the simulation outputs to understand main effects, interactions, and optimal settings.

Computer simulation outputs (continuous numerical responses)

Santner, T. J., Williams, B. J., & Notz, W. I. (2003). The Design and Analysis of Computer Experiments. Springer.

Kleijnen, J. P. C. (2015). Design and Analysis of Simulation Experiments (2nd ed.). Springer.


Simulation-assisted event tree analysis

Monte Carlo ETA
stochastic event tree analysis
simulation-enhanced ETA
probabilistic event tree simulation

1970s–1990s (formalized in probabilistic risk assessment practice)

H.A. Watson (Bell Telephone Laboratories, ETA origins ~1961); Monte Carlo integration of ETA developed in nuclear/aerospace PRA community 1970s–1990s

Simulation-assisted event tree analysis (ETA) extends classical event tree analysis by replacing fixed point-estimate branch probabilities with Monte Carlo or discrete-event simulation. This allows analysts to propagate uncertainty through every branch of the tree and obtain full probability distributions over accident sequences and system outcomes, yielding far richer risk insights than deterministic ETA alone.

Probabilistic risk and reliability assessment method

Component failure rates, conditional branch probabilities, simulation-generated samples

Zio, E. (2009). Reliability engineering: Old problems and new challenges. Reliability Engineering and System Safety, 94(2), 125–141.

event-tree-analysis fault-tree-analysis failure-mode-and-effects-analysis simulation-assisted-fault-tree-analysis bayesian-event-tree-analysis risk-based-event-tree-analysis


Simulation-Assisted Ex Post Facto Research Design

Ex post facto: 1964; simulation-assisted hybrid: 1990s–2000s

Kerlinger, F. N. (ex post facto basis); simulation integration drawn from computational social science (Axelrod, Epstein, 1990s)

Simulation-assisted ex post facto design is a non-experimental observational approach in which the researcher examines already-occurred events or conditions using existing records and then supplements the empirical analysis with computational simulation to approximate counterfactual scenarios that cannot be observed in reality. The design retains the retrospective, naturalistic character of classic ex post facto research while leveraging agent-based, Monte Carlo, or system-dynamics simulation to address the inherent confound limitations of purely archival work.

Non-experimental observational design with computational augmentation

Existing records, archival data, administrative datasets, supplemented by simulation-generated counterfactual data

Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston.

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.

ex-post-facto-design causal-comparative-design quasi-experimental-design agent-based-modeling monte-carlo-simulation retrospective-cohort-study


Simulation-assisted failure mode and effects analysis

Simulation-Assisted Failure Mode and Effects Analysis

Simulation-FMEA
Monte Carlo FMEA
Simulation-based FMEA
SA-FMEA

1949 (FMEA); simulation-assisted variant: 1980s–1990s

FMEA originates from US MIL-P-1629 (1949); simulation integration developed in reliability engineering from the 1980s–1990s

Simulation-assisted FMEA enhances the classical Failure Mode and Effects Analysis by replacing point-estimate occurrence ratings with probabilistic simulation — typically Monte Carlo — to quantify failure probability distributions across a system's components. This yields statistically grounded Risk Priority Numbers (RPNs) rather than expert guesses, enabling more rigorous identification and prioritization of critical failure modes in complex engineering systems.

Failure rate data, probability distributions, engineering system models

Stamatis, D. H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution (2nd ed.). ASQ Quality Press.

Failure mode and effects analysis. Wikipedia.


Simulation-assisted fault tree analysis

SA-FTA
Monte Carlo FTA
simulation-based FTA
stochastic fault tree analysis

1970s–1980s (widespread adoption in nuclear and aerospace industries)

Fault tree analysis: H. A. Watson (Bell Labs, 1961); Monte Carlo integration in reliability: Herman Kahn / Stanislaw Ulam (RAND, late 1940s); combination formalized in reliability engineering literature from the 1970s onward

Simulation-assisted fault tree analysis (SA-FTA) combines the logical structure of classical fault tree analysis with Monte Carlo or discrete-event simulation to estimate the probability and timing of an undesired top event when component failures follow complex, non-exponential, or correlated probability distributions. The approach overcomes the analytical limitations of Boolean algebra-based FTA and is widely used in nuclear, aerospace, chemical process, and manufacturing reliability engineering.

Quantitative reliability and risk analysis technique

Component failure rate data, probability distributions, logic gate structures

Vesely, W. E., Goldberg, F. F., Roberts, N. H., & Haasl, D. F. (1981). Fault Tree Handbook. US Nuclear Regulatory Commission, NUREG-0492.

Zio, E. (2013). The Monte Carlo Simulation Method for System Reliability and Risk Analysis. Springer.

fault-tree-analysis failure-mode-and-effects-analysis event-tree-analysis reliability-analysis monte-carlo-simulation risk-based-fault-tree-analysis


Simulation-Assisted Fractional Factorial Design

computer-aided fractional factorial design

FFD: 1950s; simulation integration: 1980s–2000s

Box, Hunter & Hunter (FFD basis); Kleijnen and others (simulation integration)

Simulation-assisted fractional factorial design (SA-FFD) combines the statistical efficiency of fractional factorial experimentation with computerized simulation models to screen and estimate factor effects when physical experiments are too costly, hazardous, or time-consuming. A carefully chosen subset of factor-level combinations — the fractional factorial array — is executed inside a validated simulation model instead of (or alongside) a real process, dramatically reducing resource requirements while preserving the ability to identify main effects and low-order interactions.

Experimental design with computational augmentation

Simulated output responses (continuous or discrete); factor settings from fractional factorial array

Kleijnen, J. P. C. (2008). Design and Analysis of Simulation Experiments. Springer.

Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.


Simulation-Assisted Full Factorial Design of Experiments

SA-FFD
computer simulation full factorial
virtual full factorial design
simulation-based full factorial DOE

1990s–2000s (simulation-DOE integration formalized)

Montgomery (DOE foundations); Kleijnen (simulation DOE formalization)

Simulation-assisted full factorial design integrates full factorial design of experiments (DOE) with computer simulation models — such as discrete-event simulation, finite element analysis, or Monte Carlo methods — to systematically explore every combination of factor levels and quantify their effects on system responses. It enables comprehensive experimentation in contexts where physical trials would be costly, dangerous, or infeasible.

Experimental design with computer simulation

Simulation output (continuous or discrete numerical responses)


Simulation-Assisted Hypothesis Testing Research

simulation-based hypothesis testing
Monte Carlo hypothesis testing
computational hypothesis testing
simulation-assisted inference

1980s–1990s (bootstrap: 1979; permutation inference: mid-20th century; unified simulation-assisted framing: 1990s–2000s)

Bradley Efron (bootstrap framework); Phillip Good (permutation tests); Monte Carlo tradition traced to Stanislaw Ulam and John von Neumann

Simulation-assisted hypothesis testing research replaces or supplements analytical probability theory with computational simulation — resampling, permutation, or Monte Carlo methods — to construct null distributions and evaluate hypotheses. Rather than assuming a parametric distribution and consulting a table, the researcher generates thousands of simulated datasets from the observed data or a specified model, building an empirical null distribution against which the observed test statistic is compared. The approach is especially valuable when analytic assumptions (normality, large samples) cannot be met.

Quantitative research design integrating computational simulation with classical hypothesis testing

Numeric (continuous, ordinal, count, or categorical data suitable for resampling or stochastic simulation)

Efron, B., & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Chapman and Hall/CRC.

Good, P. I. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer.

monte-carlo-simulation bootstrap-resampling permutation-test power-analysis randomization-test bayesian-hypothesis-testing


Simulation-Assisted Process Capability Analysis

Monte Carlo process capability
simulation-based Cpk analysis
stochastic capability analysis
virtual process capability study

1980s–1990s (mature practice by mid-1990s)

Developed through integration of Monte Carlo simulation with classical capability indices (Juran, Kane, Kotz and colleagues)

Simulation-assisted process capability analysis combines Monte Carlo simulation with classical capability indices (Cp, Cpk, Cpm) to evaluate whether a process can consistently meet specification limits when direct measurement is costly, dangerous, or impractical. By propagating input distributions through a process model, the analyst obtains a simulated output distribution and derives capability metrics without waiting for physical production runs. The approach is especially valuable during product design, process scale-up, and tolerance stack-up studies.

Simulated continuous process output distributions; specification limits

Kotz, S., & Lovelace, C. R. (1998). Process Capability Indices in Theory and Practice. Arnold.

Rubinstein, R. Y., & Kroese, D. P. (2016). Simulation and the Monte Carlo Method (3rd ed.). Wiley.

process-capability-analysis statistical-process-control monte-carlo-simulation design-of-experiments robust-process-capability-analysis six-sigma-dmaic


Simulation-Assisted Quality Function Deployment

SA-QFD
simulation-integrated QFD
simulation-driven house of quality
QFD with simulation

1990s–2000s (QFD: 1966; simulation integration: ~1995–2005)

Yoji Akao (QFD foundation); simulation integration developed by engineering researchers in 1990s–2000s

Simulation-assisted quality function deployment (SA-QFD) integrates computational simulation into the classic QFD framework to replace or supplement costly physical prototypes when evaluating how engineering design decisions satisfy customer requirements. By embedding simulation models — such as finite element analysis, discrete-event simulation, or system dynamics — within the House of Quality matrix, engineers can rapidly quantify the impact of technical characteristics on customer satisfaction and iteratively refine design priorities before committing to production.

Hybrid engineering design and quality planning method

Customer requirement ratings, engineering characteristic data, simulation output metrics

Akao, Y. (Ed.). (1990). Quality Function Deployment: Integrating Customer Requirements into Product Design. Productivity Press.

Park, T., & Kim, K. J. (2003). Determination of an optimal set of design requirements using house of quality. Journal of Operations Management, 21(2), 133–146.


Simulation-Assisted Quantitative Content Analysis

computational content analysis with simulation

Extension of Neuendorf (2002) and Krippendorff (2018) quantitative content analysis traditions, with simulation augmentation developed within computational social science

Simulation-assisted quantitative content analysis (SA-QCA) extends classical quantitative content analysis by integrating computational simulation — typically Monte Carlo methods or agent-based models — to validate coding schemes, estimate coder reliability under controlled conditions, test category distinctiveness, and assess the robustness of frequency-based conclusions before or alongside the analysis of real text corpora. The method preserves the systematic, replicable counting logic of quantitative content analysis while adding a simulation layer that strengthens methodological rigour.

Quantitative / computational research method

Text corpora (media, documents, social media), plus simulated synthetic text samples

Neuendorf, K. A. (2002). The Content Analysis Guidebook. Sage Publications.

Krippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology (4th ed.). Sage Publications.

quantitative-content-analysis computational-text-analysis monte-carlo-simulation intercoder-reliability automated-content-analysis mixed-methods-research


SARA
Monte Carlo reliability analysis
simulation-based reliability assessment
virtual reliability testing

1940s–1980s (Monte Carlo foundations ~1940s; simulation-reliability integration ~1970s–1980s)

Enrico Fermi, John von Neumann, Stanislaw Ulam (Monte Carlo foundations); Freudenthal (structural reliability); Melchers (simulation integration)

Simulation-assisted reliability analysis combines probabilistic reliability theory with computational simulation — most commonly Monte Carlo methods or finite-element models — to estimate the probability that a system, component, or structure will perform its intended function under uncertain operating conditions. Rather than relying solely on closed-form analytical solutions, it propagates uncertainty through high-fidelity numerical models to quantify failure risk across complex, nonlinear, or multi-failure-mode systems.

Quantitative probabilistic engineering method

Numerical data, probability distributions, physics-based or empirical failure models

Melchers, R. E., & Beck, A. T. (2018). Structural Reliability Analysis and Prediction (3rd ed.). Wiley.


Simulation-Assisted Response Surface Methodology

SA-RSM
simulation-based RSM
computer simulation RSM
metamodel-assisted RSM

1951 (RSM); simulation integration widely adopted from 1980s onward

Box & Wilson (RSM foundation); Kleijnen and others for simulation-based extensions

Simulation-assisted response surface methodology (SA-RSM) combines computer simulation models — such as finite element analysis, computational fluid dynamics, or discrete-event simulation — with the statistical framework of response surface methodology to efficiently map, model, and optimize system responses. Instead of running physical experiments, the researcher executes simulation runs at design points prescribed by an RSM design, fits a polynomial metamodel (surrogate) to the simulation outputs, and uses that metamodel to locate optimal factor settings.

Computer simulation output (continuous numerical responses)

Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley.


Simulation-assisted root cause analysis

Sim-RCA
simulation-based RCA
virtual root cause analysis
computational root cause analysis

1990s–2000s (widespread adoption in engineering reliability contexts)

Evolved from root cause analysis practice (Kepner & Tregoe, 1960s) integrated with simulation methods (1990s–2000s in reliability engineering)

Simulation-assisted root cause analysis (Sim-RCA) integrates computational simulation — such as discrete-event simulation, Monte Carlo methods, or finite-element analysis — into the structured root cause analysis process to diagnose the underlying causes of complex failures or defects. By running virtual experiments on a system model, investigators can test hypothetical causal pathways safely, rapidly, and at scale, without disrupting live operations or waiting for rare failure events to recur.

Analytical / diagnostic engineering method

Process data, failure records, system parameters, simulation model outputs

Latino, R. J., & Latino, K. C. (2006). Root Cause Analysis: Improving Performance for Bottom-Line Results (3rd ed.). CRC Press.

Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall.


Simulation-assisted Six Sigma DMAIC

Simulation-Assisted Six Sigma DMAIC (Define-Measure-Analyze-Improve-Control)

Sim-DMAIC
Simulation-integrated DMAIC
Six Sigma with simulation
DMAIC simulation modeling

2000s–present (systematic integration of simulation with DMAIC)

Integration practice emerged from industrial engineering and operations research communities; DMAIC framework originates with Motorola/GE Six Sigma (1980s–1990s)

Simulation-assisted Six Sigma DMAIC embeds discrete-event or Monte Carlo simulation models inside the classic DMAIC cycle (Define, Measure, Analyze, Improve, Control) to test process changes virtually before committing to physical implementation. By running thousands of simulated scenarios, teams quantify variation, identify bottlenecks, and verify improvement hypotheses at low cost and with minimal disruption to live operations.

Process performance data, simulation output (continuous and discrete)

Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). John Wiley & Sons.

Harrell, C., Ghosh, B. K., & Bowden, R. O. (2011). Simulation Using ProModel (3rd ed.). McGraw-Hill.


Simulation-Assisted Statistical Process Control

Simulation-based SPC
Monte Carlo SPC
SA-SPC
Simulation-integrated SPC

Walter A. Shewhart (SPC foundations); simulation integration developed through industrial engineering literature from the 1980s onward

Simulation-assisted statistical process control (SA-SPC) combines computer simulation — typically Monte Carlo or discrete-event simulation — with classical SPC methods to design, test, and calibrate control charts and monitoring schemes before or alongside deployment on a real production process. Rather than relying solely on closed-form analytical assumptions, SA-SPC uses simulated data to evaluate chart performance under realistic, often non-normal process conditions.

Simulated process data, historical process measurements, control chart statistics

Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). Wiley.

Jensen, W. A., Jones-Farmer, L. A., Champ, C. W., & Woodall, W. H. (2006). Effects of parameter estimation on control chart properties: A literature review. Journal of Quality Technology, 38(4), 349–364.

statistical-process-control control-chart monte-carlo-simulation design-of-experiments process-capability-analysis six-sigma-dmaic


Simulation-assisted Taguchi method

Simulation-assisted Taguchi Robust Parameter Design

virtual Taguchi design
simulation-based Taguchi
Taguchi-simulation integration
SA-Taguchi

1980s (Taguchi method); simulation integration prominent from 1990s–2000s

Genichi Taguchi (Taguchi method); simulation integration developed across engineering literature from 1990s

The simulation-assisted Taguchi method replaces or supplements physical prototypes with computer simulation models (finite element analysis, computational fluid dynamics, discrete-event simulation, etc.) to execute Taguchi orthogonal-array experiments. Signal-to-noise ratios and effects are computed from virtual runs, enabling rapid, low-cost optimisation of design parameters for robustness against noise factors — all before any physical hardware is built.

Simulated (virtual) response data from computer models (FEA, DES, CFD, Monte Carlo, etc.)

Phadke, M. S. (1989). Quality Engineering Using Robust Design. Prentice Hall.

Antony, J., & Kaye, M. (2006). Experimental quality: a strategic approach to achieve and improve quality. Springer Science & Business Media.


Simulation-Assisted Trend Research

Simulation-Assisted Trend Research Design

simulation-based longitudinal trend design

1990s–2000s (convergence of computational simulation with survey-based trend designs)

Synthesized from trend research (Creswell) and Monte Carlo / agent-based simulation traditions (Mooney, 1997)

Simulation-assisted trend research combines repeated cross-sectional survey data collected at multiple time points with computational simulation techniques — such as Monte Carlo methods or agent-based modeling — to project, validate, and stress-test observed trends. It extends classic trend research by replacing or supplementing extrapolation with probabilistic scenario modeling, allowing researchers to quantify uncertainty around trend trajectories and explore counterfactual futures under varying assumptions.

Quantitative research design with computational augmentation

Repeated cross-sectional survey data; simulated data from probabilistic models

Creswell, J. W., & Creswell, J. D. (2023). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (6th ed.). SAGE Publications.

Mooney, C. Z. (1997). Monte Carlo Simulation. SAGE Publications. (Quantitative Applications in the Social Sciences, No. 116).


Simulation-Based Power Analysis

Simulation-Based Power Analysis (Monte Carlo Power)

Simülasyon Tabanlı Güç Analizi (Monte Carlo Power)

Arnold et al. (2011); Green & MacLeod (2016) for mixed-model extension

Simulation-based power analysis estimates the statistical power and required sample size of a study by repeating a full analysis pipeline thousands of times on artificially generated data. Because it relies on Monte Carlo simulation rather than closed-form equations, it is applicable to designs — mixed models, complex measurement structures, non-standard outcomes — where analytical power formulas do not exist. The approach was systematically described for applied research by Arnold et al. in 2011, and the mixed-model implementation via the SIMR package was formalised by Green and MacLeod in 2016.

Arnold, B.F. et al. (2011). Simulation Methods to Estimate Design Power: An Overview for Applied Research. BMC Medical Research Methodology, 11, 94.

Green, P. & MacLeod, C.J. (2016). SIMR: An R Package for Power Analysis of Generalized Linear Mixed Models by Simulation. Methods in Ecology and Evolution, 7(4), 493–498.

bayesian-power-analysis power-analysis-ttest power-analysis-multilevel sequential-analysis independent-t-test one-way-anova


DASH

Debriefing Assessment for Simulation in Healthcare

DASH Scale
Simulation Debriefing Assessment
Debriefing Feedback Scale

Jeffrey W. Rudolph, Robert Simon, Daniel B. Raemer

The DASH is a 20-item observer-rated instrument measuring the quality of debriefing—the structured, facilitated reflection following a healthcare simulation activity. Developed by Rudolph, Simon, and Raemer in 2006 at Massachusetts General Hospital, the DASH evaluates the debriefing facilitator's ability to create a psychological safety environment, elicit reflection on events, establish learning objectives, and foster insight into clinical decision-making. The scale is widely used in medical and nursing education to assess the fidelity and effectiveness of simulation-based learning experiences.

Rudolph, J. W., Simon, R., Dufresne, R. L., & Raemer, D. B. (2006). There's no such thing as 'nonjudgmental' debriefing: A theory and method for debriefing with good judgment. Simul Healthc 1(1): 49–55.

Cheng, A., Grant, V., Currie, G., Hecker, K., Driller, J., & Robinson, T. (2014). Manikin-based simulation for rapid sequence induction course: Experience with a mixed interprofessional and prior experience audience. Acad Emerg Med 19(5): 627–638.

ripls clinical-learning-environment-scale professional-identity-scale reflective-practice-questionnaire


Simultaneous Localization and Mapping

SLAM
Concurrent Mapping and Localization

Simultaneous Localization and Mapping (SLAM) is the problem of enabling a mobile robot to build a map of its environment while simultaneously determining its own location within that map using noisy sensor measurements. Formulated by Durrant-Whyte and Bailey in 2006, SLAM is fundamental to autonomous robotics, enabling robots to navigate and explore unknown environments without prior maps or external positioning systems.

Durrant-Whyte, H., & Bailey, T. (2006). Simultaneous localization and mapping (SLAM): Part I. IEEE Robotics & Automation Magazine, 13(2), 99-110.

Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic Robotics. MIT Press.

Dellaert, F., & Kaess, M. (2012). Square root SAM: Simultaneous localization and mapping via square root factor graphs. International Journal of Robotics Research, 25(12), 1181-1203.


SIMUS

SIMUS (Sequential Interactive Model for Urban Systems) is a ranking multi-criteria decision-making (MCDM) method introduced by Munier, N. in 2011. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Linear programming sequential optimization of criteria objectives

Munier, N. (2011). A Strategy for Using Multicriteria Analysis in Decision-Making: A Guide for Simple and Complex Environmental Projects. Springer


Single-blind AB Design

Single-blind AB Single-Subject Experimental Design

assessor-blind AB design
single-masked AB single-case design
observer-blind AB phase design

1960s (AB methodology); blinding adaptation in single-case research developed from 1970s onward

Murray Sidman; Baer, Wolf & Risley (AB logic); blinding conventions adapted from clinical trial methodology

The single-blind AB design is a single-subject experimental design that combines the two-phase AB structure — a baseline phase (A) followed by an intervention phase (B) — with assessor or observer masking. The individual collecting or rating outcome data is kept unaware of which phase is being measured, preventing knowledge of treatment status from biasing behavioral observations or ratings. The design improves on the standard AB design by reducing detection bias while retaining the practical and ethical advantages of single-subject methodology.

Single-subject experimental design with assessor masking

Repeatedly measured behavioral, clinical, or functional outcome data over time

Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press.

Kratochwill, T. R., Hitchcock, J., Horner, R. H., Levin, J. R., Odom, S. L., Rindskopf, D. M., & Shadish, W. R. (2010). Single-case designs technical documentation. What Works Clearinghouse. Retrieved from https://ies.ed.gov/ncee/wwc/Docs/ReferenceResources/wwc_scd.pdf


Single-blind A/B test

single-masked A/B test
single-blind split test
blinded two-condition experiment
participant-blind A/B test

mid-20th century (blinded RCT framework); A/B test nomenclature ~1990s–2000s

Fisher, R. A. (randomisation basis); blinding practice formalised in clinical trials mid-20th century

A single-blind A/B test is a controlled two-condition experiment in which participants are randomised to condition A (control) or condition B (treatment) but are kept unaware of which condition they have received, while researchers and analysts remain aware. The blind prevents participants from changing their behaviour in response to knowledge of their assignment, reducing demand characteristics and response bias while still allowing the investigator to monitor the trial.

Controlled experiment with partial blinding

Continuous, binary, or count outcome data collected from two randomised groups

Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press.

Schulz, K. F., & Grimes, D. A. (2002). Blinding in randomised trials: hiding who got what. The Lancet, 359(9307), 696–700.

double-blind-ab-test ab-design randomized-controlled-trial multi-arm-experiment blocked-ab-test adaptive-ab-test


Single-blind ABA Design

single-blind reversal design
single-masked ABA design
single-blind withdrawal design
assessor-blind ABA design

1968 (ABA design); single-blind adaptation developed through 1970s–1980s clinical behavioral research

Montrose Wolf, Donald Baer, Todd Risley (ABA tradition); single-blind masking adapted from clinical trial methodology

The single-blind ABA design combines the three-phase reversal logic of the ABA single-subject design — baseline (A1), intervention (B), and withdrawal (A2) — with single-blind masking, in which outcome assessors are kept unaware of the current phase or treatment condition while the participant and intervention team remain aware. This blinding reduces observer bias in behavioral measurement across the three phases.

Single-subject experimental design with assessor blinding

Continuous behavioral observation data (frequency, rate, duration, intensity); observer-rated outcome measures

Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91–97.

aba-design abab-design single-blind-ab-design double-blind-aba-design multiple-baseline-design single-subject-experimental-design


Single-blind ABAB design

single-blind reversal design
single-masked ABAB design
blinded single-case reversal design
single-blind withdrawal design

1960s (ABAB logic); single-blind adaptation formalized in applied behavior analysis from 1970s onward

B. F. Skinner (reversal logic); blinding conventions adapted from clinical trial methodology

The single-blind ABAB design is a single-case experimental approach that sequences two baseline phases (A) and two intervention phases (B) to demonstrate experimental control over a target behavior, while keeping one party — typically the outcome assessor or the participant — unaware of current phase assignment. This blinding procedure reduces observer bias or demand characteristics, strengthening the internal validity of the reversal logic.

Single-case experimental design with partial blinding

Repeated behavioral observations, continuous or interval measures across time

Barlow, D. H., Nock, M. K., & Hersen, M. (2009). Single Case Experimental Designs: Strategies for Studying Behavior Change (3rd ed.). Pearson.

abab-design aba-design ab-design multiple-baseline-design single-subject-experimental-design double-blind-abab-design


Single-blind control group experimental design

Single-blind Control Group Experimental Design

single-masked controlled experiment
single-blind controlled trial
SB-CGD
single-blind parallel-group design

Mid-20th century (blinding standards consolidated ~1950s–1970s)

Classical experimental tradition; blinding formalized in 20th-century clinical trial methodology

A single-blind control group experimental design is a controlled experiment in which participants are kept unaware of whether they are receiving the active treatment or a control condition, while researchers and outcome assessors remain unmasked. The design uses a designated control group as the baseline for comparison, allowing causal inference about the treatment effect while limiting participant-driven response biases such as the placebo effect and demand characteristics.

Quantitative outcome measures (continuous, ordinal, or categorical)

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.

Schulz, K. F., Altman, D. G., & Moher, D. (2010). CONSORT 2010 statement: Updated guidelines for reporting parallel group randomised trials. BMJ, 340, c332.


Single-blind Factorial Experiment

single-masked factorial trial
single-blind factorial design
SB factorial experiment

Factorial design: 1926; single-blinding as systematic practice: mid-20th century

Fisher, R. A. (factorial design); blinding practices formalized in clinical trials literature (20th century)

A single-blind factorial experiment combines factorial design — simultaneously varying two or more independent factors across all their level combinations — with single-blinding, in which participants are unaware of which treatment condition they have been assigned to while researchers and administrators remain unmasked. This design enables efficient estimation of main effects and interactions while reducing participant-side response bias.

Continuous, ordinal, or binary outcome measurements across multiple factor combinations

Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.


Single-blind field experiment

single-masked field experiment
field experiment with single blinding
single-blind natural-setting trial

Mid-20th century (blinding conventions formalised 1940s–1960s)

Established practice in experimental social science and clinical research; codified by Campbell & Stanley (1963) and Shadish, Cook & Campbell (2002)

A single-blind field experiment combines real-world experimental conditions with partial blinding: either participants or outcome assessors — but not both — are kept unaware of treatment assignment. This design reduces demand characteristics or observer bias while preserving ecological validity, making it a practical middle ground when full double-blinding is logistically infeasible in naturalistic settings.

Controlled field experiment with partial blinding

Quantitative outcome measures collected in real-world settings


Single-blind Fractional Factorial Experimental Design

single-masked fractional factorial
single-blind FFD
partially blinded fractional factorial
single-blind 2^(k-p) design

1940s–1950s (fractional factorial foundations); blinding conventions formalised through 1960s–1980s

Fractional factorial theory: R. L. Plackett & J. P. Burman (1946); single-blinding practice codified in clinical trial methodology (20th century)

A single-blind fractional factorial experiment studies multiple factors simultaneously by testing only a strategically chosen subset — a fraction — of all possible factor-level combinations, while keeping participants unaware of which treatment condition they receive. This design yields substantial information about main effects and selected interactions at a fraction of the cost of a full factorial experiment, with single-blinding reducing participant-side response bias.

Continuous, ordinal, or binary outcome measures collected under masked treatment assignment

Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley-Interscience.


Single-blind Full Factorial Experiment

Single-blind Full Factorial Experimental Design

single-masked full factorial
single-blind complete factorial
SB-FFE
single-blind all-combinations design

Full factorial: 1935 (Fisher); single-blind clinical convention: mid-20th century

Full factorial framework: R. A. Fisher; single-blind masking practice: clinical trial tradition, standardized by the 20th century

A single-blind full factorial experiment systematically tests every combination of all factor levels while keeping participants unaware of their treatment assignment. This design allows simultaneous estimation of all main effects and all interaction effects between factors, with single-blind masking reducing participant-side biases such as demand characteristics and expectation effects — without requiring investigator blinding.

Continuous, ordinal, or categorical outcome measures (quantitative)


Single-blind laboratory experiment

single-masked laboratory study
participant-blind lab experiment
single-blind controlled lab study

Late 19th century; codified in 20th-century clinical and behavioral research

Formalized in experimental psychology and pharmacology; Peirce & Jastrow (1884) early instance

A single-blind laboratory experiment is a controlled study conducted in a laboratory setting in which participants do not know which condition (e.g., treatment or control) they have been assigned to, while the researchers administering the conditions are aware. This masking of participants reduces demand characteristics and response bias without requiring full investigator blinding, and the controlled laboratory environment allows tight manipulation of independent variables and precise measurement of outcomes.

Continuous, ordinal, or categorical outcome measures collected in a controlled laboratory setting


Single-blind multi-arm experiment

Single-blind Multi-arm Experimental Design

unidirectional blinding multi-arm design

Developed within the clinical trials tradition; formalized by Friedman, Furberg, and DeMets and others in the 20th century

A single-blind multi-arm experiment is a controlled experimental design that simultaneously compares three or more treatment conditions while blinding participants — but not investigators — to their group assignment. This configuration reduces response bias driven by participants' expectations, preserves operational feasibility when full blinding is impractical, and allows direct pairwise and omnibus comparisons across multiple arms within a single study.

Continuous, categorical, or ordinal outcome data; requires treatment assignment and outcome measurement per arm

Friedman, L. M., Furberg, C. D., & DeMets, D. L. (2010). Fundamentals of Clinical Trials (4th ed.). Springer.


Single-blind Natural Experiment

single-masked natural experiment
blinded quasi-experiment
single-blind exogenous assignment study

Conceptual synthesis of natural experiment tradition (Haavelmo, 1944; Campbell & Stanley, 1963) with single-blind methodology

A single-blind natural experiment leverages an exogenous, researcher-uncontrolled event — such as a policy change, lottery, or natural disaster — to create treatment and comparison groups, while applying single-blind procedures so that either the participants or the outcome assessors (but not both) are unaware of group assignment. This design combines the causal leverage of natural variation with reduced measurement bias from blinding.

Quasi-experimental design with partial blinding

Observational or administrative data; outcome assessor is blinded to group assignment

Dunning, T. (2012). Natural Experiments in the Social Sciences: A Design-Based Approach. Cambridge University Press.


Single-blind pretest-posttest experimental design

Single-Blind Pretest-Posttest Experimental Design

single-masked pretest-posttest design
participant-blind pretest-posttest
single-blind before-after design
SB-PP design

1963 (systematic codification); blinding in use from early 20th century

Campbell & Stanley (codified); blinding practice has earlier roots in clinical research

The single-blind pretest-posttest experimental design combines two protective strategies: measuring outcomes both before and after treatment to quantify change, and keeping participants unaware of which condition they are in. This pairing controls for preexisting group differences and expectancy-driven response bias, making it a practical middle ground between fully open-label and double-blind trials in behavioral and health research.

Controlled experimental design with partial blinding

Quantitative outcome measures (pre- and post-intervention scores, continuous or ordinal)

Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.


Single-blind Randomized Controlled Trial

single-masked RCT
single-blind RCT
single-blind trial
SB-RCT

1948 (formalized); single-blind variant established in mid-20th century clinical trial methodology

Bradford Hill and colleagues (MRC streptomycin trial, 1948); blinding conventions codified in CONSORT guidelines

A single-blind randomized controlled trial (SB-RCT) is a rigorous experimental design in which participants are randomly assigned to treatment or control conditions while remaining unaware of which condition they have received. Investigators, outcome assessors, and data analysts are not blinded. By masking participants, the design eliminates placebo and nocebo response biases on the participant side, while preserving investigator flexibility to administer and monitor the intervention.

Experimental design — blinded randomized trial

Quantitative outcome measures (clinical, behavioral, or performance data)

Schulz, K. F., Altman, D. G., Moher, D., & CONSORT Group. (2010). CONSORT 2010 statement: Updated guidelines for reporting parallel group randomised trials. BMJ, 340, c332.


Single-blind single-subject experimental design

Single-blind Single-Subject Experimental Design

single-blind single-case experimental design

Barlow & Hersen (single-subject methodology); blinding conventions from clinical trial tradition

A single-blind single-subject experimental design (SB-SSED) applies a single-blind protocol to an N-of-1 experiment: one individual participant is studied intensively across alternating or sequential phases, and either the participant or the assessor — but not both — is kept unaware of the current treatment condition. This design combines the idiographic power of single-subject methodology with a structured blinding control to reduce performance or assessment bias, and is common in applied behavior analysis, clinical psychology, and rehabilitation research.

Repeated behavioral, physiological, or psychological measurements on one individual

Barlow, D. H., & Hersen, M. (1984). Single case experimental designs: Strategies for studying behavior change (2nd ed.). Pergamon Press.

Kazdin, A. E. (2011). Single-case research designs: Methods for clinical and applied settings (2nd ed.). Oxford University Press.


Single-Case Study

single-site case study
holistic single-case design
intrinsic case study
bounded case inquiry

1984 (Yin's seminal protocol); 1995 (Stake's art-of-case-study framework)

A single-case study is a qualitative research design that investigates one bounded instance — an organization, program, event, individual, or community — in its real-world context through multiple converging sources of evidence. Developed into a rigorous social-science method chiefly by Robert Yin and Robert Stake, it is especially powerful when the case is unique, extreme, critical, or revelatory, and when the research question begins with 'how' or 'why' rather than 'how many.'

Interviews, documents, observations, archival records, artifacts

1 case (unit of analysis); multiple data sources within that case

Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.

Stake, R. E. (1995). The Art of Case Study Research. Sage.

case-study ethnography phenomenology action-research grounded-theory narrative-analysis


Single-cell ChIP-seq peak calling

Single-cell Chromatin Immunoprecipitation Sequencing Peak Calling

scChIP-seq peak calling
single-cell chromatin profiling
scChIC-seq analysis
single-cell epigenomic peak detection

Grosselin et al.; Ku et al. (parallel independent development)

Single-cell ChIP-seq peak calling is a bioinformatics pipeline that identifies genomic regions enriched for histone modifications or transcription factor binding in individual cells. By profiling chromatin states at single-cell resolution, it reveals epigenomic heterogeneity hidden in bulk ChIP-seq experiments, enabling researchers to map regulatory landscapes across distinct cell populations within a complex tissue sample.

Single-cell ChIP-seq or ChIC-seq reads (FASTQ/BAM)

Grosselin, K., Durand, A., Marsolier, J., Poitou, A., Marangoni, E., Nemati, F., ... & Vallot, C. (2019). High-throughput single-cell ChIP-seq identifies heterogeneity of chromatin states in breast cancer. Nature Genetics, 51(6), 1060-1066.

Ku, W. L., Nakamura, K., Gao, W., Cui, K., Hu, G., Tang, Q., ... & Zhao, K. (2019). Single-cell chromatin immunocleavage sequencing (scChIC-seq) to profile histone modification. Nature Methods, 16(4), 323-325.


Single-cell Copy Number Variation Analysis

single-cell copy number aberration analysis

Navin et al. (single-cell sequencing for CNV); Garvin et al. (Ginkgo tool, 2015)

Single-cell copy number variation (scCNV) analysis detects gains and losses of genomic segments within individual cells, enabling researchers to resolve intratumor heterogeneity, reconstruct clonal evolution, and distinguish malignant from normal cells at single-cell resolution. It can be applied to single-cell whole-genome sequencing data directly or inferred from read-depth signals in scRNA-seq or scATAC-seq experiments.

Single-cell whole-genome sequencing (scWGS), single-cell RNA-seq (scRNA-seq), or single-cell ATAC-seq read depth data

Garvin, T., Aboukhalil, R., Kendall, J., Baslan, T., Atwal, G. S., Hicks, J., Wigler, M., & Schatz, M. C. (2015). Interactive analysis and assessment of single-cell copy-number variations. Nature Methods, 12(11), 1058–1060.

Gao, R., Bai, S., Henderson, Y. C., Lin, Y., Schalck, A., Yan, Y., Kumar, T., Hu, M., Sei, E., Davis, A., Wang, F., Shaitelman, S. F., Wang, J. R., Chen, K., Moulder, S., Lai, S. Y., & Navin, N. E. (2021). Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes. Nature Biotechnology, 39(5), 599–608.


Single-cell epigenome-wide association study

Single-Cell Epigenome-Wide Association Study

single-cell chromatin accessibility EWAS

2015–2020 (methodology consolidation period)

Developed through convergence of EWAS methodology (Rakyan et al., 2011) and single-cell epigenomics (Buenrostro et al., 2015)

A single-cell epigenome-wide association study (scEWAS) interrogates epigenetic marks — primarily DNA methylation or chromatin accessibility — across the entire genome at single-cell resolution, then statistically associates variation in those marks with a phenotype, disease, or exposure. By resolving cell-type heterogeneity that bulk EWAS cannot separate, scEWAS identifies epigenetic signals that are specific to rare or intermixed cell populations rather than averaged across tissues.

Single-cell ATAC-seq, single-cell bisulfite sequencing, or single-cell CUT&TAG data; phenotype/trait annotations per cell or cell cluster

Zhang, Y., et al. (2022). Single-cell epigenome analysis reveals age-associated decay of heterochromatin domains in excitatory neurons in the mouse brain. Cell Research, 32(1), 1-18.

Aryee, M. J., et al. (2014). Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics, 30(10), 1363-1369.

epigenome-wide-association-study single-cell-atac-seq single-cell-rna-seq chromatin-accessibility-analysis dna-methylation-analysis multi-omics-integration


Single-cell eQTL analysis

Single-cell Expression Quantitative Trait Loci Analysis

sc-eQTL analysis
single-cell eQTL mapping
scRNA-seq eQTL
cell-type-specific eQTL

Cuomo et al.; Kim-Hellmuth et al. (pioneering sc-eQTL frameworks, 2020)

Single-cell eQTL analysis identifies genetic variants (eQTLs) that regulate gene expression in a cell-type-specific manner by jointly analysing single-cell RNA-seq profiles and donor genotype data. Unlike bulk eQTL methods, it resolves regulatory effects that are diluted or masked when cell types are mixed, enabling discovery of variants whose effects are confined to particular cell states or developmental stages.

Single-cell RNA-seq gene expression + genotype data (SNP arrays or WGS)

Cuomo, A. S. E., et al. (2020). Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression. Nature Communications, 11(1), 810.

Kim-Hellmuth, S., et al. (2020). Cell type–specific genetic regulation of gene expression across human tissues. Science, 369(6509), eaaz8528.

eqtl-analysis single-cell-rna-seq-analysis rna-seq-differential-expression genome-wide-association-study single-cell-gwas pathway-enrichment-analysis


Single-cell Gene Set Enrichment Analysis

scGSEA
single-cell GSEA
cell-level gene set scoring
scRNA-seq pathway scoring

Sara Aibar, Stein Aerts (AUCell/SCENIC); David DeTomaso, Nir Yosef (VISION)

Single-cell gene set enrichment analysis (scGSEA) extends classical bulk GSEA to the resolution of individual cells. Rather than testing whether a gene set is enriched in a sample-level comparison, scGSEA assigns an enrichment or activity score to each cell, enabling researchers to map pathway activity across heterogeneous cell populations, cell states, and developmental trajectories captured in single-cell RNA-seq data.

Computational enrichment scoring pipeline

Single-cell RNA-seq count matrices; curated gene sets (MSigDB, GO, KEGG)

Aibar, S., Gonzalez-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., van den Oord, J., Kalender Atak, Z., Wouters, J., & Aerts, S. (2017). SCENIC: Single-cell regulatory network inference and clustering. Nature Methods, 14(11), 1083-1086.

DeTomaso, D., Jones, M. G., Subramaniam, M., Ashuach, T., Ye, C. J., & Yosef, N. (2019). Functional interpretation of single cell similarity maps. Nature Communications, 10(1), 4376.


Single-cell GWAS

Single-Cell Genome-Wide Association Study

single-cell genetic association analysis

2019–2022 (rapid emergence with large-scale scRNA-seq atlases)

Multiple groups (Price lab, De Jager lab, others); scDRS framework by Zhang et al. 2022

Single-cell GWAS is an integrative bioinformatics pipeline that maps genome-wide association study (GWAS) signals onto single-cell transcriptomic landscapes to identify which cell types and individual cells carry disproportionate genetic risk for a disease or trait. By leveraging single-cell RNA-seq atlases alongside GWAS summary statistics, it moves beyond tissue-level associations to reveal the precise cellular contexts in which disease-associated genetic variants exert their effects.

Single-cell RNA-seq data + GWAS summary statistics

Zhang, M. J., Hou, K., Dey, K. K., Sakaue, S., Jagadeesh, K. A., Weinand, K., ... & Price, A. L. (2022). Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data. Nature Genetics, 54(8), 1224-1234.

Bryois, J., Calini, D., Macnair, W., Foo, L., Urich, E., Ortmann, W., ... & De Jager, P. L. (2022). Cell-type-specific cis-eQTLs in eight human brain cell types identify novel risk genes for psychiatric and neurological disorders. Nature Neuroscience, 25(8), 1104-1112.

genome-wide-association-study single-cell-rna-seq-analysis eqtl-analysis single-cell-eqtl-analysis pathway-enrichment-analysis rna-seq-differential-expression


Single-cell metabolomics analysis

single-cell mass spectrometry metabolomics

2013–2021 (emerging field; major methods established ~2019–2021)

Multiple groups; key early platforms: Alexandrov lab (SpaceM), Bhatt/Bhattacharya groups

Single-cell metabolomics analysis measures the small-molecule metabolite content of individual cells, revealing cell-to-cell metabolic heterogeneity that bulk methods obscure by averaging. Rooted in mass spectrometry and microfluidics advances, it enables researchers to map metabolic states across cell populations, identify rare subpopulations, and link metabolic phenotypes to cellular function — providing a functional complement to transcriptomics and proteomics at single-cell resolution.

Single-cell mass spectrometry data (MALDI-MS, nano-DESI, microfluidics-MS, or fluorescent metabolic reporters)

Rappez, L., Stadler, M., Triana, S., Gathungu, R. M., Ovchinnikova, K., Phapale, P., Heikenwalder, M., & Alexandrov, T. (2021). SpaceM reveals metabolic states of single cells. Nature Methods, 18(7), 799–805.

Zhu, H., Zou, G., Wang, N., Zhuang, M., Xiong, W., & Huang, G. (2021). Single-neuron identification of chemical constituents, physiological changes, and metabolism using mass spectrometry. Proceedings of the National Academy of Sciences, 118(3), e2010377118.

metabolomics-analysis single-cell-rna-seq-analysis single-cell-proteomics-analysis multi-omics-metabolomics-analysis pathway-enrichment-analysis mass-spectrometry-imaging


Single-cell Microbiome Diversity Analysis

Single-cell Resolution Microbiome Diversity Analysis

sc-microbiome analysis
single-cell microbial profiling
single-bacterium sequencing
microSPLiT analysis

Paul Blainey lab and Bhatt lab (pioneered microSPLiT and single-microbe genomics approaches)

Single-cell microbiome diversity analysis resolves the composition and functional heterogeneity of microbial communities at the level of individual cells or bacteria. By combining single-cell or single-bacterium isolation with high-throughput sequencing, this pipeline overcomes the averaging effect of bulk metagenomics, enabling detection of rare strains, intra-species variation, and cell-to-cell heterogeneity within complex microbiomes such as the gut, oral cavity, or environmental samples.

Single-cell or single-bacterium sequencing reads (16S rRNA, shotgun metagenomics, or transcriptomics at single-cell resolution)

Kehe, J., Kulesa, A., Ortiz, A., Ackerman, C. M., Thakku, S. G., Sellers, D., Bhatt, S., ... & Blainey, P. C. (2019). Massively parallel screening of synthetic microbial communities. Proceedings of the National Academy of Sciences, 116(26), 12804-12809.

Zheng, W., Zhao, S., Yin, Y., Zhang, H., Needham, D. M., Evans, E. D., Bhatt, S., ... & Bhatt, D. L. (2020). High-throughput, single-microbe genomics with strain resolution, applied to a human gut microbiome. Science, 376(6597), eabm1483.


Single-cell Phylogenetic Analysis

Single-cell Phylogenetic and Lineage Tree Reconstruction

scPhylogeny
single-cell lineage tracing
clonal phylogenetics
single-cell tree inference

Multiple groups; foundational tools: Trapnell et al. (Monocle, 2014), Jones et al. (Cassiopeia, 2020)

Single-cell phylogenetic analysis reconstructs evolutionary or developmental trees from single-cell sequencing data, tracing how individual cells diverged from a common ancestor. By leveraging somatic mutations, CRISPR-introduced barcodes, or copy-number changes as heritable characters, this method maps clonal relationships within tumors, developing tissues, or immune repertoires with unprecedented cellular resolution.

Computational phylogenetic inference pipeline

Single-cell DNA/RNA sequencing data, CRISPR lineage barcodes, somatic mutations

Jones, M. G., Khodaverdian, A., Quinn, J. J., Chan, M. M., Hussmann, J. A., Wang, R., Xu, C., Weissman, J. S., & Yosef, N. (2020). Inference of single-cell phylogenies from lineage tracing data using Cassiopeia. Genome Biology, 21(1), 92.

Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014). The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nature Biotechnology, 32(4), 381-386.

phylogenetic-analysis single-cell-rna-seq-analysis copy-number-variation-analysis trajectory-inference variant-calling clonal-evolution-analysis


Single-cell RNA-seq analysis

scRNA-seq
single-cell transcriptomics
scRNAseq analysis
single-cell gene expression profiling

2009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016

Azim Surani, Barbara Treutlein, and the Regev/McCarroll groups (foundational droplet-based methods ~2015)

Single-cell RNA sequencing (scRNA-seq) analysis characterises gene expression at the resolution of individual cells, enabling discovery of cell types, states, and transitions that are invisible in bulk transcriptomics. Starting from raw sequencing reads, the workflow produces a cell-by-gene count matrix and proceeds through quality control, normalisation, dimensionality reduction, unsupervised clustering, cell-type annotation, and a range of downstream analyses such as trajectory inference and differential expression between cell populations.

High-throughput single-cell transcriptomic profiling pipeline

Single-cell or single-nucleus raw sequencing reads (FASTQ); count matrices

Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015). Spatial reconstruction of single-cell gene expression data. Nature Biotechnology, 33(5), 495–502.

Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., ... & McCarroll, S. A. (2015). Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell, 161(5), 1202–1214.

rna-seq-differential-expression pathway-enrichment-analysis gene-set-enrichment-analysis single-cell-variant-calling single-cell-eqtl-analysis single-cell-pathway-enrichment-analysis


Single-Cell RNA Sequencing Differential Expression Analysis

cell-level differential expression analysis

2013–2015 (first scRNA-seq DE tools; refined 2015–present)

Pioneered through Seurat (Satija lab) and scde (Kharchenko lab) frameworks, building on bulk RNA-seq DE foundations

Single-cell RNA-seq differential expression (scRNA-seq DE) analysis identifies genes whose expression levels differ significantly between defined groups of individual cells — such as cell types, disease states, or treatment conditions. Unlike bulk RNA-seq, which averages signals across millions of cells, scRNA-seq DE operates on the transcriptome of each individual cell, enabling fine-grained characterization of cell-population-specific gene regulation and heterogeneity within seemingly homogeneous tissue.

Single-cell RNA sequencing count matrices (UMI or read counts per cell per gene)

Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018). Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature Biotechnology, 36(5), 411–420.

Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550.

rna-seq-differential-expression single-cell-rna-seq-analysis gene-set-enrichment-analysis pathway-enrichment-analysis cluster-analysis dimensionality-reduction


Single-cell sequence alignment

scRNA-seq alignment
single-cell read mapping
scSeq alignment
cell barcode-aware alignment

Adapted from bulk RNA-seq aligners; single-cell extensions by Dobin et al. (STAR) and 10x Genomics Cell Ranger team

Single-cell sequence alignment is the computational step that maps millions of short sequencing reads produced by single-cell RNA-seq experiments back to a reference genome or transcriptome. Unlike bulk RNA-seq alignment, each read carries a cell barcode and a Unique Molecular Identifier (UMI) that together identify the originating cell and the individual RNA molecule. Accurate alignment and barcode demultiplexing are prerequisites for constructing the cell-by-gene count matrix that drives all downstream single-cell analyses.

Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 29(1), 15–21.

Smith, T., Heger, A., & Sudbery, I. (2017). UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome Research, 27(3), 491–499.

rna-seq-differential-expression cell-clustering dimensionality-reduction umi-deduplication genome-indexing single-cell-rna-seq


Single-cell variant calling

scVariant calling
single-cell SNV calling
scDNA-seq variant detection
single-cell somatic mutation calling

2016 (Monovar; foundational single-cell SNV calling)

Hamim Zafar, Ken Chen, Nicholas Navin and colleagues

Single-cell variant calling is a bioinformatics pipeline that identifies DNA sequence variants — single-nucleotide variants (SNVs), small insertions and deletions, and copy-number alterations — within individual cells rather than across a bulk tissue mixture. By resolving the mutational landscape cell by cell, it reveals intra-tumoral heterogeneity, clonal architecture, and somatic mutation patterns that bulk sequencing obscures. The approach is central to cancer genomics, developmental biology, and any study where cell-to-cell genetic diversity is the primary question.

Single-cell DNA or RNA sequencing reads (FASTQ/BAM)

Zafar, H., Wang, Y., Nakhleh, L., Navin, N., & Chen, K. (2016). Monovar: single-nucleotide variant detection in single cells. Nature Methods, 13(6), 505–507.

Singer, J., Ruscheweyh, H. J., Doherr, M. G., Stadler, T., & Althaus, C. L. (2021). Single-nucleotide variant calling in single-cell sequencing data with piccolo. BMC Bioinformatics, 22(1), 333.

single-cell-rna-sequencing copy-number-variation-analysis bulk-variant-calling somatic-mutation-detection clonal-evolution-analysis phylogenetic-tree-inference


Single-Subject Experimental Design

SSED
single-case experimental design
n-of-1 design
intrasubject replication design

1960s (Sidman 1960; formal applied codification by Kazdin and Baer in 1970s–1980s)

Murray Sidman (foundational tactics); B. F. Skinner (applied behavior analysis lineage)

Single-subject experimental design (SSED) establishes experimental control by repeatedly measuring one individual (or a small number of individuals) across baseline and intervention phases, using the participant as their own control. Instead of comparing groups, it compares the participant's own behavior across conditions over time. Widely used in applied behavior analysis, special education, rehabilitation, and clinical psychology, SSED allows causal inference from small or unique samples where group designs are impractical.

Repeated measures of behavioral or performance outcomes over time (continuous observation data)

Kazdin, A. E. (1982). Single-Case Research Designs: Methods for Clinical and Applied Settings. Oxford University Press.

Sidman, M. (1960). Tactics of Scientific Research: Evaluating Experimental Data in Psychology. Basic Books.

ab-design aba-design abab-design multiple-baseline-design case-study pretest-posttest-experimental-design


Singular Spectrum Analysis

Singular Spectrum Analysis (SSA) is a nonparametric method for time-series decomposition and forecasting based on singular value decomposition (SVD) of a time-lagged embedding matrix. Introduced by Broomhead and King (1986) and developed further by Vautard, Yiou, and Ghil (1992), SSA decomposes time series into trend, oscillatory, and noise components without assuming any underlying model. It is particularly effective for short, noisy non-stationary signals where parametric approaches fail.

Dimension reduction and trend extraction

Broomhead, D. S., & King, G. P. (1986). Extracting qualitative dynamics from experimental data. Physica D: Nonlinear Phenomena, 20(2–3), 217–236.

Vautard, R., Yiou, P., & Ghil, M. (1992). Singular-spectrum analysis: A toolkit for short, noisy chaotic signals. Physica D: Nonlinear Phenomena, 58(1–4), 95–126.

Golyandina, N., Nekrutkin, V., & Zhigljavsky, A. (2001). Analysis of Time Series Structure: SSA and Related Techniques. Chapman and Hall/CRC.

principal-component-analysis kernel-pca independent-component-analysis singular-value-decomposition


Singular Value Decomposition

Singular Value Decomposition (SVD) is a fundamental matrix factorization technique that decomposes any m × n matrix A into the product A = U Σ V^T, where U and V are orthogonal matrices and Σ is a diagonal matrix of singular values. Developed by Gene Golub and others in the 1960s–1970s, SVD is the most robust method for analyzing matrix structure and solving linear systems.

Golub, G. H., & Kahan, W. (1970). Calculating the singular values and pseudo-inverse of a matrix. Journal of the SIAM Series B: Numerical Analysis, 2(2), 205–224.

Golub, G. H., & Van Loan, C. F. (1983). Matrix computations (2nd ed.). Johns Hopkins University Press.

Trefethen, L. N., & Bau, D. (1997). Numerical Linear Algebra. SIAM.

principal-component-analysis eigenvalue-decomposition qr-decomposition matrix-factorization


The SNOT-22 is a 22-item disease-specific quality-of-life questionnaire designed to assess sino-nasal symptoms and their functional impact on patients with chronic rhinosinusitis, nasal polyposis, and allied conditions. Developed by Hopkins and colleagues at King's College London in 2009, it has become the most widely used instrument for measuring sino-nasal disease burden in clinical trials and rhinological practice. The SNOT-22 provides rapid, patient-centered assessment of both nasal-specific symptoms (congestion, drainage, sneezing) and general health impacts (sleep, headache, concentration).

Hopkins, C., Gillett, S., Slack, R., Lund, V. J., & Browne, J. P. (2009). Psychometric validity of the 22-item Sinonasal Outcome Test. Clinical Otolaryngology, 34(5), 447-454.

Kennedy, J. L., Hubbard, M. A., Huyett, P., Patanavanich, S., Gould, H. M., & Köller, D. Y. (2012). Sino-Nasal Outcome Test (SNOT-22): A multicenter validation study. Rhinology, 50(4), 359-363.


SIR Model

Kermack–McKendrick Model
Susceptible-Infectious-Recovered Model
Compartmental Epidemic Model
SIR Epidemiyoloji Modeli

The SIR model is a foundational mathematical framework for describing the spread of infectious diseases through a population. Introduced by William Ogilvy Kermack and Anderson Gray McKendrick in 1927, it partitions a closed population of size N into three mutually exclusive compartments: Susceptible (S), Infectious (I), and Recovered (R). A system of ordinary differential equations governs the flow of individuals between compartments, capturing epidemic dynamics with two key parameters — the transmission rate β and the recovery rate γ.

Population size, contact rate, recovery rate

Output: Epidemic trajectory over time

Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society A, 115(772), 700–721.


SIR

SIR (Superiority and Inferiority Ranking) is a outranking multi-criteria decision-making (MCDM) method introduced by Xu, X. in 2001. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Xu, X. (2001). The SIR method: A superiority and inferiority ranking method for multiple criteria decision making. European Journal of Operational Research

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


Site Index Curve

A site index curve is a family of curves relating tree height to stand age, used to quantify the productivity of a forest site. Site index is conventionally defined as the height of dominant trees at a reference age (typically 50 years in temperate forests). These curves enable foresters to classify sites by productivity class and to predict growth rates for planning timber harvests and silvicultural treatments. Site index curves are among the most fundamental tools in forest growth and yield modeling.

Clutter, J. L., Fortson, J. C., Pienaar, L. V., Brister, G. H., & Bailey, R. L. (1983). Timber Management: A Quantitative Approach. John Wiley & Sons.

Davis, L. S., Johnson, K. N., Bettinger, P. S., & Howard, T. E. (2001). Forest Management to Sustain Ecological, Economic, and Social Values. McGraw-Hill.


Situational Awareness Rating Technique

The Situational Awareness Rating Technique (SART), developed by Robert Taylor in 1990 for the NATO Advisory Group for Aerospace Research and Development (AGARD), is a subjective post-task measurement instrument for assessing an operator's degree of situational awareness (SA)—the perception of elements in the environment, understanding of their meaning, and projection of their future state. SART is widely used in aviation, military operations, emergency response, and human-factors research to evaluate system designs, training effectiveness, and task demands that enable or impair operator situational awareness.

Taylor, R. M. (1990). Situational awareness rating technique (SART): The development of a tool for aircrew systems design. In AGARD-CP-478 (pp. 3/1–3/17). NATO Advisory Group for Aerospace Research and Development.

nasa-task-load-index team-situation-awareness operator-performance-scale workload-profile


SIWEC

SIWEC (Simple Weight Calculation) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Puška, A. Nedeljković, M. Pamučar, D. Božanić, D. Simić, V. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Direct expert scoring with dispersion-weighted aggregation; no pairwise comparison or criterion ranking required.

Puška, A., Nedeljković, M., Pamučar, D., Božanić, D., Simić, V. (2024). Application of the new simple weight calculation (SIWEC) method in the case study in the sales channels of agricultural products. MethodsX

aras aroman artasi cobra cocoso codas compromise-programming copras


Six-Minute Walk Test

The Six-Minute Walk Test (6MWT) is a submaximal exercise assessment measuring the total distance a person can walk in six minutes at a self-selected pace. Developed by Guyatt and colleagues in 1985, the 6MWT has become the standard submaximal functional exercise test for patients with cardiopulmonary disease, quantifying exercise tolerance and predicting outcomes in conditions ranging from chronic heart failure to pulmonary hypertension to neuromuscular disorders.

Guyatt, G. H., Sullivan, M. J., Thompson, P. J., Fallen, E. L., Pugsley, S. O., Taylor, D. W., & Berman, L. B. (1985). The 6-minute walk: A new measure of exercise tolerance in patients with chronic heart failure. Canadian Medical Association Journal, 132(8), 919-923.

American Thoracic Society. (2002). ATS statement: Guidelines for the six-minute walk test. American Journal of Respiratory and Critical Care Medicine, 166(1), 111-117.


Six Sigma DMAIC

DMAIC Framework
Six Sigma Process Improvement Cycle
Define-Measure-Analyze-Improve-Control
Altı Sigma DMAIC

Six Sigma DMAIC is a data-driven, five-phase process improvement methodology — Define, Measure, Analyze, Improve, and Control — used to reduce defects and process variation to fewer than 3.4 defects per million opportunities. Originating at Motorola in the 1980s and systematized by practitioners including Pyzdek and Keller, it is widely adopted in manufacturing, healthcare, finance, and service industries seeking sustained quality gains.

Structured process improvement methodology

6σ (±6 standard deviations within specification limits)

Pyzdek, T., & Keller, P. (2014). The Six Sigma Handbook (4th ed.). McGraw-Hill.


Six Sigma in Healthcare

Six Sigma Quality Improvement Methodology Applied to Healthcare

Quality management, Process optimization

Six Sigma is a data-driven quality improvement methodology originating at Motorola in 1986 that aims to reduce process variation and defects to achieve near-perfect quality (3.4 defects per million opportunities). In healthcare, Six Sigma uses statistical analysis and structured project methodology (DMAIC: Define-Measure-Analyze-Improve-Control) to reduce errors, improve safety, and enhance patient outcomes.

Statistical quality improvement methodology

Harry, M. J., & Schroeder, R. (2000). Six Sigma: The Breakthrough Management Strategy. Currency.

Pyzdek, T. (2003). The Six Sigma Handbook (2nd ed.). McGraw-Hill.

Tennant, G., & Field, A. (2007). Six Sigma and Lean: Convergence and Implications. The Quality Engineer, 17(4), 327–337.

lean-healthcare patient-flow-simulation balanced-scorecard-healthcare dea-hospital-efficiency clinical-audit


Skew-T Log-P Analysis

Skew-T Log-P Thermodynamic Diagram Analysis

Skew-T
Skew-T Log-P diagram
Tephigram
Stuve diagram

The Skew-T Log-P diagram is a thermodynamic chart used extensively in meteorology to visualize atmospheric profiles of temperature, dew point, and pressure. Developed in its modern form by Reitan in the 1960s, it allows forecasters and researchers to quickly assess atmospheric stability, convective potential, wind shear, and other properties critical for weather diagnosis and prediction.

Bluestein, H. B. (1992). Synoptic-dynamic meteorology in midlatitudes. Volume I: Principles of Kinematics and Dynamics. Oxford University Press.

Stull, R. B. (2011). Wet-bulb temperature from relative humidity and air temperature. Journal of Applied Meteorology and Climatology, 50(11), 2267-2273.


Skindex-29 Skin Disease Quality of Life Instrument

Skindex-29 is a validated, patient-centered quality-of-life measure specifically designed to assess the impact of any skin disease on patients' symptoms, emotions, and functioning. Developed by Chren, Lasek, and colleagues in 1997, it captures the multidimensional burden of dermatological conditions beyond clinical severity. Skindex-29 is widely used in clinical trials, observational studies, and dermatology practice to ensure that treatment efficacy encompasses quality-of-life outcomes.

Chren MM, Lasek RJ, Quinn LM, et al. Skindex, a quality-of-life measure for patients with skin disease: reliability, validity, and responsiveness. J Invest Dermatol. 1997;107(5):707-713.

Chren MM, Lasek RJ, Flocke SA, Zyzanski SJ. Improved discriminative and evaluative capability of a refined version of Skindex, a quality-of-life instrument for patients with skin diseases. Arch Dermatol. 1997;133(11):1433-1440.


Slag Basicity

Slag Basicity Index for Pyrometallurgical Processes

Slag basicity is a measure of the composition of slag formed during smelting and roasting operations. It is typically expressed as the ratio of basic oxides (CaO, MgO) to acidic oxides (SiO2). Basicity controls slag fluidity, viscosity, and reactivity, directly affecting metal recovery, processing temperature, and product quality. It is a critical parameter in copper, nickel, and lead smelting.

Slag composition parameter for controlling roast/smelt conditions

Barnes, J. F., Edwards, C. C., & Sims, R. L. (2010). Copper smelting and refining: pyrometallurgical fundamentals. JOM, 52(12), 38-43.

Davenport, W. G., King, M., Schlesinger, M., & Biswas, A. K. (2002). Extractive metallurgy of copper (4th ed.). Pergamon Press.


SLEDAI
SLEDAI-2K
SLE Disease Activity Index

The SLEDAI is a comprehensive clinician-assessed measure of systemic lupus erythematosus (SLE) disease activity, capturing manifestations across multiple organ systems (cutaneous, renal, neuropsychiatric, hematologic, and serological). Introduced by Bombardier et al. (1992) and refined as SLEDAI-2K by Gladman et al. (2002), SLEDAI uses weighted scoring of 24 clinical and laboratory features to quantify overall SLE activity. It is the most widely used outcome measure in SLE research and clinical trials, enabling standardised assessment of disease progression, flare prediction, and treatment response in this complex multisystem disease.

Gladman DD, Ibañez D, Urowitz MB. Systemic Lupus Erythematosus Disease Activity Index 2000. The Journal of Rheumatology. 2002;29(2):288-291.

Bombardier C, Gladman DD, Urowitz MB, Caron D, Chang CH. Derivation of the SLEDAI: a disease activity index for lupus patients. The Committee on Prognosis Studies in SLE. Arthritis & Rheumatism. 1992;35(6):630-640.


SCI

Sleep Condition Indicator
SCI Insomnia Scale

Insomnia symptom assessment; DSM-5 aligned

Espie, C. A., Kyle, S. D., Hames, P., et al.

The Sleep Condition Indicator (SCI) is an 8-item self-report scale designed to assess the severity of insomnia symptoms in adults in a manner closely aligned with DSM-5 diagnostic criteria for insomnia disorder. Developed by Espie and colleagues in 2014, it measures the core features of insomnia: difficulty initiating sleep, difficulty maintaining sleep, early morning awakening, daytime impairment, and associated distress. The SCI is increasingly used in clinical practice and research to screen for insomnia, monitor treatment response, and evaluate cognitive-behavioral therapy efficacy.

Espie, C. A., Kyle, S. D., Hames, P., Cbermack, R. A., & Morin, C. M. (2014). A randomized, placebo-controlled trial of online cognitive behavioral therapy for chronic insomnia disorder delivered via a mobile application. Sleep, 37(9), 1553-1563.