Validation
Validation refers to the systematic process of confirming that a system, process, method, product, or claim meets predefined criteria, requirements, or intended purposes through evidence-based evaluation and testing.[1] This confirmation ensures reliability, relevance, and suitability for use, distinguishing validation from verification, which focuses on whether something is built correctly, while validation assesses whether the right thing has been built.[2] Across disciplines, validation serves as a cornerstone for establishing credibility and trustworthiness, often involving quantitative comparisons, empirical data, or logical assessment to mitigate risks and support decision-making.[3] In engineering and technology, validation is integral to model verification and validation (V&V), where it quantifies the accuracy of simulations or predictions against real-world observations to ensure computational models can reliably inform design and analysis.[4] For instance, in software development, validation evaluates whether the final product fulfills user needs and stakeholder expectations in its operational environment, typically through testing protocols like user acceptance testing.[5] In regulatory contexts such as toxicology or pharmaceuticals, validation confirms the reliability of analytical methods or processes by comparing them against established standards, enabling safe and effective applications.[6][7] In scientific research, particularly psychology, validation involves establishing the truth or logical soundness of measurements, theories, or instruments, such as verifying a psychological test's accuracy in assessing intended constructs like hope or self-worth.[8] Emotional validation, a related interpersonal practice, entails acknowledging and empathizing with an individual's feelings to foster trust and reduce distress, even without agreement.[9] This approach is evidenced in therapeutic settings to support mental health outcomes.[10] In legal and philosophical domains, validation pertains to the enforceability and legitimacy of norms or rules, where legal validity determines a law's binding force based on its alignment with higher-order criteria like constitutional rules of recognition.[11] Philosophically, it extends to justifying obligations or truths through moral or rational consensus, ensuring systems of authority remain justified and corrigible.[12]General Concept
Definition
Validation is the act or process of confirming that an entity—such as data, a process, a system, or an emotion—meets predefined criteria, standards, or expectations through evidence-based assessment. This confirmation establishes the accuracy, compliance, or acceptability of the subject in question, ensuring it fulfills its intended purpose or application. According to the ISO 9000 family of standards, validation specifically refers to "confirmation, through the provision of objective evidence, that the requirements for a specific intended use or application have been fulfilled." The term "validation" derives from the Latin validus, meaning "strong" or "effective," which relates to having legal or binding force. It entered English in the mid-17th century as a noun form of the verb "validate," borrowed from Medieval Latin validatus (past participle of validare, "to make valid") via French valider. This etymological root underscores the concept's emphasis on strengthening or substantiating something's legitimacy.[13] Central to validation processes are key principles of objectivity, reproducibility, and documentation. Objectivity requires assessments to rely on verifiable evidence rather than personal bias, as highlighted in quality management standards that mandate "objective evidence" for confirmation. Reproducibility ensures that the validation methods can be repeated under similar conditions to yield consistent outcomes, a foundational aspect of scientific and technical reliability. Documentation involves maintaining detailed records of procedures, results, and evidence to support transparency and future audits.[14][15] Validation can be categorized into formal and informal types. Formal validation entails structured, often regulatory-compliant procedures with rigorous testing and certification to meet external standards, such as in quality assurance frameworks. In contrast, informal validation involves less structured acknowledgment or personal confirmation without mandatory documentation, such as in everyday interpersonal contexts. Verification, a related but distinct process, focuses on checking internal consistency or correctness against specifications, whereas validation assesses alignment with broader user needs or expectations.[16]Historical Development
The concept of validation traces its philosophical origins to ancient Greece, where Aristotle in the 4th century BCE developed the theory of the syllogism as a formal method to assess the validity of deductive arguments, ensuring that conclusions necessarily follow from premises through structured logical forms.[17] This foundational approach emphasized the formal criteria for logical soundness, influencing subsequent Western thought on reasoning and proof. In medieval scholasticism, Thomas Aquinas extended these principles in the 13th century by employing Aristotelian syllogisms to validate theological and philosophical arguments, integrating faith and reason in systematic disputations to confirm the coherence of complex propositions.[18] The emergence of validation in the 19th and early 20th centuries was driven by the Industrial Revolution's shift to mass production, which necessitated quality control measures such as inspections to verify product conformity and prevent defects in factory settings.[19] A pivotal advancement came with Frederick Winslow Taylor's 1911 publication of The Principles of Scientific Management, which introduced systematic process checks and time-motion studies to validate workflow efficiency, replacing rule-of-thumb methods with data-driven optimization to enhance productivity and reliability.[20] Post-World War II developments marked a shift toward formalized standardization, with the U.S. Food and Drug Administration (FDA) establishing current Good Manufacturing Practice (cGMP) regulations in the 1960s—specifically through the 1963 guidelines—to require validation of pharmaceutical manufacturing processes, ensuring consistent safety and efficacy amid public health crises like the thalidomide scandal.[21][22] Globally, the International Organization for Standardization (ISO) released the ISO 9000 series in 1987, codifying quality management systems that emphasized validation as a core element for organizational processes across sectors.[23] Key milestones included NASA's implementation of verification and validation (V&V) protocols during the 1960s Apollo program, involving extensive subsystem testing and integrated reviews to confirm mission-critical reliability in high-stakes aerospace environments.[24] IEEE Standard 1012, first published in 1986 and revised in 1998, defined comprehensive V&V processes to evaluate software against requirements, addressing the growing complexity of digital systems.[25]Validation in Computing
Data Validation
Data validation in computing involves systematically checking input data against predefined rules and criteria to ensure its accuracy, completeness, and suitability for processing or storage in information systems.[26] This process aims to detect and prevent errors, corruption, or invalid entries that could undermine data integrity and lead to downstream issues in applications or databases.[27] By verifying data at entry points, it supports reliable decision-making and maintains the overall quality of information systems. Common techniques for data validation include format checks, which examine whether data adheres to specified patterns, such as using regular expressions to validate email addresses.[28] Range validation restricts numerical inputs to acceptable boundaries, for instance, ensuring age values fall between 0 and 120 to avoid illogical entries.[29] Referential integrity validation enforces relationships between data elements, typically through mechanisms like foreign key constraints in relational databases, to prevent orphaned or inconsistent records.[30] Implementation methods distinguish between client-side and server-side validation. Client-side validation, often executed via JavaScript in web browsers, offers real-time feedback to users for improved usability but remains vulnerable to circumvention. Server-side validation, conducted on the backend, provides robust security by independently verifying all inputs against rules, making it indispensable for protecting against malicious data.[28] In batch-oriented environments, such as Extract, Transform, Load (ETL) pipelines, validation integrates into the transformation phase to assess schema compliance and data consistency before loading into warehouses.[31] Data validation primarily targets prevalent errors like null or missing values, which indicate incomplete records; duplicates, arising from redundant entries; and outliers, representing anomalous data points that skew analyses.[32] Addressing these mitigates risks of faulty computations or biased outcomes in data-driven processes.[33] Effectiveness is evaluated using metrics such as error rates, which measure the percentage of records failing validation rules, and completeness scores, quantifying the proportion of populated fields relative to expectations.[34] These indicators help organizations track data quality improvements over time. For web applications, the OWASP Input Validation Cheat Sheet outlines best practices, including whitelist-based acceptance of inputs and context-specific encoding to thwart injection attacks.[28]Model and Algorithm Validation
Model and algorithm validation in computing involves systematically evaluating whether a computational model or algorithm generates accurate, reliable, and unbiased outputs that align with its intended purpose, ensuring generalizability beyond training data.[35] This process confirms that the model performs effectively on unseen data, mitigating risks of poor real-world deployment.[36] Key techniques for validation include k-fold cross-validation and holdout validation. In k-fold cross-validation, the dataset is partitioned into k equal-sized subsets, with the model trained on k-1 subsets and tested on the remaining one, repeating this process k times to obtain an average performance estimate; this method reduces variance in evaluation compared to single splits. The holdout method, a simpler approach, divides the data into disjoint training and testing sets, typically in an 80-20 ratio, to assess generalization directly, though it can be sensitive to the specific split. These techniques often follow data validation as a prerequisite to ensure input integrity before model assessment.[37] Performance is quantified using metrics such as precision, recall, F1-score, and receiver operating characteristic (ROC) curves, particularly for classification tasks. Precision measures the proportion of true positives among predicted positives (\text{Precision} = \frac{\text{TP}}{\text{TP + FP}}), recall captures true positives among actual positives (\text{Recall} = \frac{\text{TP}}{\text{TP + FN}}), and the F1-score, their harmonic mean (\text{F1} = 2 \times \frac{\text{Precision} \times \text{Recall}}{\text{Precision + Recall}}), balances both for imbalanced datasets.[38] For binary classification, ROC curves plot true positive rate against false positive rate across thresholds, with the area under the curve (AUC) indicating discriminative ability, where values closer to 1 signify superior performance.[39] Overall accuracy, defined as \text{Accuracy} = \frac{\text{TP + TN}}{\text{TP + TN + FP + FN}} (with TP as true positives, TN as true negatives, FP as false positives, and FN as false negatives), provides a general measure but is less informative for skewed classes.[40] Challenges in validation include overfitting, where models memorize training data at the expense of generalization, and algorithmic bias, which can perpetuate unfair outcomes. Overfitting is addressed through regularization techniques, such as L2 regularization that adds a penalty term (\lambda \sum w_i^2) to the loss function to constrain model complexity and favor simpler solutions. Bias in AI models, arising from skewed training data, requires mitigation to comply with regulations like the EU AI Act (Regulation (EU) 2024/1689), which mandates high-risk systems undergo bias detection and correction in training, validation, and testing datasets to ensure fairness and non-discrimination. As of 2025, emerging practices include AI-driven automation for real-time model validation and bias detection in machine learning pipelines.[41] These validation practices are applied in machine learning pipelines to automate iterative model tuning and deployment, ensuring end-to-end reliability in production environments. In simulation software, validation verifies that models accurately replicate real-world dynamics, often through statistical comparisons between simulated and empirical outputs, supporting applications in engineering and scientific modeling.[42]Validation in Engineering
Verification and Validation
In engineering, verification refers to the process of confirming that a system, component, or product is built correctly by evaluating it against specified design requirements and technical standards, often summarized by the question "Are we building the product right?"[43] This involves checking for compliance through objective evidence, such as reviews of design documents or performance metrics, to ensure internal consistency and adherence to predefined criteria.[44] In contrast, validation assesses whether the completed product fulfills its intended purpose and meets user needs in the operational environment, encapsulated as "Are we building the right product?"[43] It focuses on the system's effectiveness in real-world scenarios, bridging the gap between technical specifications and stakeholder expectations.[45] These processes are interdependent, with verification providing the foundation for reliable validation, and together they mitigate risks of defects or misalignments that could compromise safety and performance.[43] Key frameworks guide the application of verification and validation in engineering projects. The V-model, which emerged in the 1960s as a structured approach to systems development, depicts verification activities on the descending left side (design and implementation phases) and validation on the ascending right side (testing and deployment phases), emphasizing parallel planning of development and quality assurance.[46] This model promotes traceability from requirements to testing, reducing errors through early detection. The IEEE 1012 standard, updated in 2024, outlines comprehensive processes for verification and validation planning across systems, software, and hardware, including integrity levels based on consequence and likelihood to tailor activities proportionally to risk.[43] It specifies activities like concept verification and system validation, ensuring systematic documentation and reporting to support certification and compliance.[43] Methods for verification typically include inspections (detailed examination of artifacts), walkthroughs (informal peer reviews), analysis (mathematical or simulation-based checks), demonstration (observing functionality), and testing (controlled execution under defined conditions), selected based on requirement complexity and resource availability.[47] For validation, techniques such as user testing (gathering feedback from end-users), prototype evaluations (iterative assessments of mockups), and operational simulations (mimicking real environments) are employed to confirm usability and fitness for purpose.[48] These methods are often combined; for instance, prototypes may undergo both verification against design specs and validation through user trials to iteratively refine the product.[49] The integration of verification and validation varies by methodology. In traditional waterfall approaches, these activities follow a sequential flow, with verification occurring during development phases and validation at the end, which can delay issue resolution but ensures comprehensive documentation.[50] Conversely, agile methodologies incorporate iterative V&V throughout sprints, enabling continuous feedback and adaptation, though this requires robust tools for traceability to maintain rigor.[51] This shift supports faster cycles in dynamic projects while preserving quality. A notable case is the Boeing 787 Dreamliner program in the 2000s, where extensive outsourcing led to integration challenges and validation gaps, contributing to over three years of delays, cost overruns exceeding $30 billion, and certification hurdles due to unaddressed system interactions.[52] The project's V&V processes ultimately highlighted the need for stronger supplier coordination and early validation to align innovative composites and avionics with operational demands.[53]System and Process Validation
System validation in engineering entails end-to-end testing of the fully integrated system to confirm it fulfills stakeholder requirements and performs as intended in its operational environment.[54] According to ISO/IEC/IEEE 15288, this process provides objective evidence that the system complies with specified needs through activities such as acceptance testing, which evaluates the system in real or simulated operational conditions with end-users.[54] Verification serves as the complementary step, focusing on whether the system is built correctly, while validation ensures it is the right system for the purpose.[54] Process validation focuses on establishing that operational processes are repeatable and consistently produce results within defined specifications, particularly by analyzing sources of variability.[7] Design of Experiments (DOE) is a key statistical method used in this context, enabling engineers to identify critical process parameters, their interactions, and ranges that minimize variability while maintaining quality outputs.[7] This approach supports the development of robust processes by revealing relationships between inputs and outputs through controlled experimentation.[7] The validation of systems and processes typically progresses through structured stages: Installation Qualification (IQ), which verifies that equipment and systems are installed correctly according to design specifications; Operational Qualification (OQ), which tests operations across defined ranges to ensure functionality; and Performance Qualification (PQ), which demonstrates consistent performance under actual or simulated production conditions using multiple batches or runs.[7] These stages build cumulative evidence of reliability, with IQ focusing on setup integrity, OQ on operational consistency, and PQ on long-term reproducibility.[7] Risk-based approaches guide the prioritization and scope of validation efforts, as outlined in the ICH Q9 guidelines, by assessing potential impacts on safety and quality to allocate resources efficiently.[55] Tools such as Failure Mode and Effects Analysis (FMEA) from ICH Q9 help identify and mitigate risks in process design and system integration.[55] Simulation software like MATLAB and Simulink facilitates virtual end-to-end testing and variability analysis by modeling multidomain systems, enabling fault injection, coverage assessment, and predictive validation before physical implementation.[56] In automotive engineering, system validation is exemplified by crash testing, where full-scale vehicle impacts under Federal Motor Vehicle Safety Standards (FMVSS) confirm the integrated structure's ability to protect occupants during collisions, such as frontal or side impacts.[57] For nuclear reactors, process validation ensures repeatable operational sequences, like control system responses to transients, through integrated testing and simulation to verify safety and performance under site-specific conditions.[58]Validation in Psychology
Emotional Validation
Emotional validation refers to the acknowledgment and acceptance of another person's emotions as understandable, legitimate, and normative within their context, without judgment, criticism, or attempts to immediately resolve or change the feelings. This process communicates that the individual's emotional response makes sense given their experiences, fostering a sense of being heard and supported. Unlike agreement with the emotion's cause, validation focuses solely on the validity of the feeling itself, promoting emotional safety and reducing the intensity of distress.[59][60][9] The roots of emotional validation trace back to Carl Rogers' person-centered therapy in the 1950s, which emphasized empathy, unconditional positive regard, and congruence as foundational elements for therapeutic change. Rogers' approach highlighted the therapist's role in mirroring and accepting the client's internal world, laying the groundwork for modern validation practices by demonstrating how nonjudgmental acceptance facilitates self-exploration and growth. Building on this, Dialectical Behavior Therapy (DBT), developed by Marsha M. Linehan in 1993, formalized validation as a core skill to balance acceptance and change in treating emotion dysregulation, particularly in borderline personality disorder. In DBT, validation involves six levels, from simple nonverbal acknowledgment to understanding the emotion's biological or historical basis, helping individuals feel less isolated in their struggles.[61][62][63] Key techniques for emotional validation include reflective listening, where one paraphrases the expressed emotion to show comprehension—for instance, responding to a frustrated colleague with, "It sounds like you're feeling overwhelmed by the deadline." In DBT, validation statements explicitly affirm the emotion's reasonableness, such as "Your anger makes sense given how that situation violated your boundaries," without endorsing problematic behaviors. These methods encourage emotional expression while maintaining boundaries, differing from problem-solving by prioritizing acceptance first.[64][63] Emotional validation offers significant benefits, including reduced emotional distress and enhanced trust in interpersonal relationships, as it signals empathy and lowers defensiveness. Research demonstrates that validation decreases negative emotional intensity, enabling better self-regulation and persistence after frustration, particularly in children. These effects contribute to improved mental health outcomes, such as lower anxiety and stronger relational bonds.[9][10][59][65] Applications of emotional validation span therapeutic settings, where it forms a pillar of DBT and other empathy-based therapies to build alliance and process trauma; parenting, where caregivers use it to model emotional literacy and de-escalate tantrums, fostering resilient children; and conflict resolution, as it diffuses tension by creating psychological safety, allowing parties to engage constructively without escalation. Cultural variations influence its practice: in collectivist societies, validation often prioritizes group harmony through interdependent emotional support, whereas in individualist cultures, it may emphasize personal autonomy and direct emotional expression. These differences highlight how validation adapts to societal norms on interdependence versus independence in emotion regulation.[66][67][68]Cognitive and Behavioral Validation
Cognitive and behavioral validation in psychological and therapeutic frameworks refers to the process of affirming the rationality, logic, or functional utility of an individual's thoughts and actions, particularly when addressing cognitive distortions or maladaptive behaviors that contribute to distress. This approach contrasts with mere acceptance by actively evaluating the evidence supporting cognitions and behaviors, often to foster more adaptive patterns. In dialectical behavior therapy (DBT), cognitive validation specifically involves recognizing and articulating the underlying beliefs, assumptions, or expectancies of a person and identifying their validity within the given context, thereby challenging unhelpful distortions without dismissal.[69][70] Key techniques include cognitive restructuring in cognitive behavioral therapy (CBT), where Socratic questioning guides individuals to examine the evidence for their thoughts, validate adaptive interpretations, and replace irrational ones with balanced alternatives. For instance, questions such as "What evidence supports this belief?" or "Are there alternative explanations?" promote self-discovery and reinforce logical thinking. Behavioral experiments complement this by testing the validity of actions through structured real-world trials; clients predict outcomes based on their beliefs, implement the behavior, and compare results to reality, often revealing the functionality or limitations of their assumptions. These methods empower individuals to build evidence-based confidence in their cognitive and behavioral processes.[71][72] Seminal frameworks underpinning these practices include Aaron Beck's cognitive therapy, developed in the 1960s, which posits that emotional disorders stem from distorted cognitions and emphasizes validating functional thoughts by modifying automatic negative ones through empirical scrutiny. In the 1980s, Steven Hayes introduced acceptance and commitment therapy (ACT), where cognitive defusion validates thoughts by framing them as transient mental events rather than absolute truths, reducing their behavioral dominance and aligning actions with personal values. Unlike emotional validation, which briefly acknowledges affective experiences as a foundational interpersonal tool, cognitive and behavioral validation prioritizes evidential logic and practical outcomes over subjective feelings.[73][74][69] Outcomes of these validation strategies include enhanced self-efficacy, as individuals gain mastery over their thoughts and behaviors through successful restructuring and experimentation, alongside significant reductions in anxiety symptoms. A 2022 meta-analysis of randomized placebo-controlled trials demonstrated CBT's efficacy in alleviating anxiety-related disorders, with a small but significant effect size (Hedges' g = 0.24), highlighting its role in promoting psychological flexibility and symptom relief.[75][76]Validation in Manufacturing
Equipment Validation
Equipment validation in manufacturing refers to the systematic verification that machinery and tools function correctly and consistently within predefined parameters, ensuring reliable performance and compliance with quality standards. This process is essential for maintaining product integrity, particularly in regulated sectors like pharmaceuticals and automotive assembly, where equipment reliability directly impacts safety and efficacy.[7] The standard framework for equipment validation consists of three sequential stages: Installation Qualification (IQ), Operational Qualification (OQ), and Performance Qualification (PQ). IQ involves confirming that the equipment is installed in accordance with manufacturer specifications, including verification of utilities, documentation, and environmental conditions.[7] OQ tests the equipment's operational capabilities across its intended ranges, such as varying speeds or temperatures, to ensure it meets functional requirements without deviations.[7] PQ evaluates the equipment's performance under actual or simulated production loads, demonstrating consistent output over multiple cycles with qualified personnel and materials.[7] These stages align with FDA guidelines under 21 CFR Parts 210 and 211 for current good manufacturing practices (CGMP), with electronic records and signatures governed by 21 CFR Part 11, originally effective in 1997 and supplemented by the October 2024 finalized guidance on electronic systems, electronic records, and electronic signatures in clinical investigations to enhance data integrity.[77][78] Key methods in equipment validation include calibration to establish and maintain accuracy, often using mathematical models such as linear regression to generate calibration curves. For instance, a simple linear model is expressed asy = mx + b
where y represents the measured response, x is the known input, m is the slope indicating sensitivity, and b is the y-intercept for offset correction; this ensures measurements align with expected values.[79] Calibrations must demonstrate metrological traceability, defined as an unbroken chain of comparisons to national reference standards like those maintained by the National Institute of Standards and Technology (NIST), to support measurement uncertainty analysis and regulatory compliance in manufacturing.[80] In pharmaceutical manufacturing, high-performance liquid chromatography (HPLC) systems exemplify equipment validation, where IQ verifies installation and connectivity, OQ assesses module precision (e.g., pump flow accuracy within ±5%), and PQ confirms holistic system performance using standardized test mixtures per USP <621> criteria, ensuring reliable analyte separation and quantification.[81] Similarly, in assembly line operations, robotic arms undergo IQ to validate mounting and power setup, OQ to test joint movements and payload capacities within operational limits, and PQ to simulate production tasks, confirming repeatability in tasks like welding or part placement to minimize defects.[82] Challenges in equipment validation include ongoing monitoring for wear and tear, which can degrade performance over time; predictive maintenance techniques, such as vibration analysis on motors and bearings, help detect early degradation to prevent failures and extend equipment life.[83] Since the early 2010s, automation trends driven by Industry 4.0 have integrated Internet of Things (IoT) sensors for real-time validation, enabling continuous data collection on parameters like temperature and vibration to automate alerts and recalibrations, thereby reducing downtime in manufacturing environments.[84]