# 3.2.4 data sampling, ISO 22989 Published contents Contents1 Scope2 Normative references3 Terms and definitions4 Abbreviated terms5 AI conceptsClause 3, terms 3.1 Terms related to AI3.1.1 AI agent3.1.2 AI component3.1.3 artificial intelligence3.1.4 artificial intelligence system3.1.5 autonomy3.1.6 application specific integrated circuit3.1.7 automatic3.1.8 cognitive computing3.1.9 continuous learning3.1.10 connectionism3.1.11 data mining3.1.12 declarative knowledge3.1.13 expert system3.1.14 general AI3.1.15 genetic algorithm3.1.16 heteronomy3.1.17 inference3.1.18 internet of things3.1.19 IoT device3.1.20 IoT system3.1.21 knowledge3.1.22 life cycle3.1.23 model3.1.24 narrow AI3.1.25 performance3.1.26 planning3.1.27 prediction3.1.28 procedural knowledge3.1.29 robot3.1.30 robotics3.1.31 semantic computing3.1.32 soft computing3.1.33 symbolic AI3.1.34 subsymbolic AI3.1.35 task3.2 Terms related to data3.2.1 data annotation3.2.2 data quality checking3.2.3 data augmentation3.2.4 data sampling3.2.5 dataset3.2.6 exploratory data analysis3.2.7 ground truth3.2.8 imputation3.2.9 input data3.2.10 label3.2.11 personally identifiable information3.2.12 production data3.2.13 sample3.2.14 test data3.2.15 validation data3.3 Terms related to machine learning3.3.1 Bayesian network3.3.2 decision tree3.3.3 human-machine teaming3.3.4 hyperparameter3.3.5 machine learning3.3.6 machine learning algorithm3.3.7 machine learning model3.3.8 parameter3.3.9 reinforcement learning3.3.10 retraining3.3.11 semi-supervised machine learning3.3.12 supervised machine learning3.3.13 support vector machine3.3.14 trained model3.3.15 training3.3.16 training data3.3.17 unsupervised machine learning3.4 Terms related to neural networks3.4.1 activation function3.4.2 convolutional neural network3.4.3 convolution3.4.4 deep learning3.4.5 exploding gradient3.4.6 feed forward neural network3.4.7 long short-term memory3.4.8 neural network3.4.9 neuron3.4.10 recurrent neural network3.5 Terms related to trustworthiness3.5.1 accountable3.5.2 accountability3.5.3 availability3.5.4 bias3.5.5 control3.5.6 controllability3.5.7 explainability3.5.8 predictability3.5.9 reliability3.5.10 resilience3.5.11 risk3.5.12 robustness3.5.13 stakeholder3.5.14 transparency3.5.15 transparency3.5.16 trustworthiness3.5.17 verification3.5.18 validation3.6 Terms related to natural language processing3.6.1 automatic summarization3.6.2 dialogue management3.6.3 emotion recognition3.6.4 information retrieval3.6.5 machine translation3.6.6 named entity recognition3.6.7 natural language3.6.8 natural language generation3.6.9 natural language processing3.6.10 natural language processing3.6.11 natural language understanding3.6.12 optical character recognition3.6.13 part-of-speech tagging3.6.14 question answering3.6.15 relationship extraction3.6.16 sentiment analysis3.6.17 speech recognition3.6.18 speech synthesis3.7 Terms related to computer vision3.7.1 computer vision3.7.2 face recognition3.7.3 image3.7.4 image recognitionClause 5, concepts 5.1 General5.2 From strong and weak AI to general and narrow AI5.3 Agent5.4 Knowledge5.5 Cognition and cognitive computing5.6 Semantic computing5.7 Soft computing5.8 Genetic algorithms5.9 Symbolic and subsymbolic approaches for AI5.10 Data5.11 Machine learning concepts Home / Vocabulary Terms related to data · 3.2.4 # data sampling choosing a subset of samples so that the patterns and trends of the larger dataset still show - definition - Defined term - ISO/IEC 22989:2022/FDAmd 1 - Last updated: 2026-10-21 ## What this term settles The term looks ordinary until two teams use it differently. The entry removes that room by naming the thing, its inputs and its limits in one sentence. ## Who it binds Nothing here creates an obligation by itself, and the obligation for data sampling arrives when the firm writes the term into a control, a specification or a purchase order. ## What to do in your practice Take the firm's own definition of data sampling, put it beside this one and mark the differences. Each difference is either a deliberate local choice or an inconsistency to fix. ## Evidence an auditor or regulator asks for - A controlled vocabulary entry for data sampling with the citation 3.2.4, an owner and a review date. An auditor can check that data sampling is controlled rather than assumed. - A data inventory field that records the entry for each dataset used in training or operation. The row names data sampling. Shows data sampling in use in the artefact the firm relies on, not only in a glossary. - A data quality record with the check, the date and the person who signed it off. The record carries the data sampling entry and its date. The data inventory is organised by system rather than by dataset, so the same data is described twice with different labels. The gap is tested against data sampling. Ask your teamIf two teams were asked what data sampling means, would their answers match this entry? ## Common gaps this page catches - data sampling is used in the data record behind every AI system without a written definition, so two teams can mean different things. - A supplier uses data sampling with a different boundary and nobody has recorded the difference. ## What to prepare before the amendment lands Brief the owners who use data sampling most often, then ask each to return one artefact that shows the entry in use. File the artefacts against the register row. ## How to test it here Take one live artefact rather than a template, find the sentence or field where data sampling is used, and put this page beside it. Record the answer, the artefact and the date, because that record is the evidence and a glossary entry on its own is only an intention. ## What this page does not claim The page does not reproduce the standard, certify the firm or predict the wording the amendment will use; it states data sampling at the claim level "definition" and stops where the public record stops. ## Common questions What does data sampling mean in ISO 22989? choosing a subset of samples so that the patterns and trends of the larger dataset still show What evidence should we keep for 3.2.4? A controlled vocabulary entry for data sampling with the citation 3.2.4, an owner and a review date. A data inventory field that records the entry for each dataset used in training or operation. The row names data sampling. Is data sampling a requirement? No: data sampling is a definition or clause of a terminology standard, and it becomes binding only when a firm writes it into a policy, a contract or a procedure, or when another standard incorporates it. ## Related units - 3.2.3 data augmentation - 3.2.5 dataset - 3.2.6 exploratory data analysis Source. ISO/IEC 22989:2022, Clause 3.2.4, Terms and definitions. Open the ISO public entry. Retrieved 21 October 2026. https://www.iso.org/obp/ui/en/#iso:std:iso-iec:22989:ed-1:v1:en ISO catalogue record 88145, the amendment status, stage and dates. Catalogue record 74296, the base standard. Claim level: definition. The definition is in the public preview. This page states it in our own words and does not reproduce the text. Built from the public ISO record and the public clause 3 entries. No clause text is reproduced. How the record is built. Last updated: 2026-10-21 · Markdown version · Check your vocabulary Source: https://iso-22989-revision-checker.theartofservice.com/glossary/data-sampling/