200 vocabulary words for age 16
- interoperability
- scalability
- extensibility
- maintainability
- observability
- testability
- deployability
- portability
- compatibility
- composability
- reusability
- modularisation
- orchestration
- containerisation
- virtualisation
- abstraction-layer
- middleware
- microservice
- monolith
- service-oriented
- event-driven
- synchronisation
- concurrency
- parallelisation
- multithreading
- multiprocessing
- distributed-consensus
- consensus-protocol
- fault-isolation
- graceful-degradation
- load-balancing
- rate-limiting
- throttling
- caching
- memoisation
- prefetching
- pagination
- sharding
- eventual-consistency
- strong-consistency
- transactional
- atomicity
- isolation
- durability
- idempotence
- commutativity
- associativity
- distributivity
- reversibility
- irreversibility
- nondeterminism
- stochastic-process
- Markov-chain
- Monte-Carlo
- bootstrapping
- cross-validation
- hyperparameter
- generalization-error
- validation-loss
- training-loss
- loss-function
- objective-function
- gradient-descent
- gradient-clipping
- learning-rate
- batch-size
- minibatch
- optimizer
- activation-function
- attention-mechanism
- self-attention
- cross-attention
- tokenisation
- tokenization
- embedding-space
- vector-space
- latent-space
- feature-space
- feature-engineering
- dimensionality-reduction
- principal-components
- singular-value-decomposition
- matrix-factorisation
- eigenvalue
- eigenvector
- orthogonality
- orthonormality
- linear-independence
- basis-vector
- span
- subspace
- nullspace
- rank
- determinant
- trace
- tensor
- scalar-product
- dot-product
- cross-product
- norm
- distance-metric
- cosine-similarity
- Euclidean-distance
- Manhattan-distance
- Mahalanobis-distance
- kernel-function
- radial-basis
- support-vector
- decision-boundary
- hyperplane
- classification-margin
- probability-calibration
- uncertainty-quantification
- conformal-prediction
- out-of-distribution
- distribution-shift
- covariate-shift
- concept-drift
- data-drift
- model-drift
- robustness-testing
- adversarial-example
- adversarial-training
- perturbation
- sensitivity-testing
- interpretability-method
- explainability-method
- feature-attribution
- attribution-map
- saliency-map
- activation-map
- counterfactual-explanation
- partial-dependence
- individual-conditional
- conditional-expectation
- causal-discovery
- causal-graph
- directed-acyclic-graph
- structural-equation
- intervention-operator
- do-calculus
- confounder-adjustment
- backdoor-criterion
- frontdoor-criterion
- instrumental-variables
- regression-discontinuity
- difference-in-differences
- synthetic-control
- interrupted-time-series
- propensity-score
- inverse-probability-weighting
- doubly-robust
- target-trial
- selection-mechanism
- missingness
- missing-at-random
- missing-not-at-random
- imputation
- multiple-imputation
- censoring
- truncation
- survivorship
- competing-risk
- hazard-ratio
- odds-ratio
- risk-ratio
- relative-risk
- absolute-risk
- number-needed-to-treat
- effect-modification
- heterogeneity-of-treatment-effect
- mediation-analysis
- moderation-analysis
- interaction-term
- multilevel-model
- hierarchical-model
- mixed-effects
- random-effects
- fixed-effects
- latent-class
- latent-growth
- growth-curve
- longitudinal-analysis
- panel-data
- time-series
- autocorrelation
- heteroskedasticity
- stationarity
- nonstationarity
- seasonality
- trend-component
- noise-component
- residual
- autocorrelation-function
- partial-autocorrelation
- autoregressive
- moving-average
- ARIMA
- forecasting-error
- mean-squared-error
These words are an advanced enrichment menu for guided discussion and research, not a required developmental vocabulary checklist.
The world as a network of relationships
An unfamiliar technology, a social problem and a scientific result can all be approached by asking what the parts are, how they interact and which evidence supports the proposed explanation. Vocabulary becomes a map for inquiry rather than a display of difficult terms.
Abstraction is selective
A useful abstraction keeps a relationship while setting aside details that do not matter for the current question. But there is no universal list of details to ignore. A model must be judged by its purpose, evidence and limitations.
Begin with an observable case
Start with a concrete example and identify what is directly known. Then introduce a technical term that clarifies the relationship. Ask which assumptions allow the explanation to work and what observations might challenge it.
Extend the idea across domains
Compare a process in technology with a process in biology or social life. Identify the structural similarity, then explain at least one important difference. The aim is transfer with care, not a claim that every system works the same way.
Space and scale
The same word may refer to a small component, an organisation or a global network. Ask how scale changes the relevant measurements, people and consequences. A model that works at one scale may fail at another.
Time and uncertainty
A forecast is not a certainty, and a historical explanation may change when new evidence emerges. Encourage learners to distinguish observations, predictions, assumptions and values. A mature vocabulary includes language for uncertainty.
Culture and responsibility
Technical decisions are made by people with different needs and experiences. Ask whose perspectives are represented in a model, who may be affected by its use and which questions cannot be settled by data alone.
From definitions to cognitive maps
Choose one word and connect it to its mechanism, inputs, outputs, assumptions, consequences and failure modes. Draw a map of the relationships. This helps learners build an organised understanding rather than memorising a string of labels.
The danger of a persuasive analogy
A metaphor can make a difficult idea approachable while hiding important distinctions. Test an analogy by naming what it explains, what it omits and what evidence would be needed before acting on it.
A practical research exercise
Find a short, reputable explanation of a contemporary technology or scientific finding. Identify the main claim, its supporting evidence and the uncertainties. Rewrite the explanation for a younger reader without losing the essential relationship.
How to recognise progress
Look for clear explanations, accurate transfer to new situations, recognition of counterexamples and willingness to revise a view. These vocabulary selections are enrichment, not a standardised measure of age-specific development.
The larger purpose
The value of vocabulary is not the number of terms remembered. It is the ability to use language to understand complexity, communicate uncertainty, connect knowledge and make responsible decisions.