simulatedata

Simulated data, or simulatedata in some contexts, refers to data produced by a model, algorithm, or synthetic process rather than gathered from real-world observation. This data is designed to mimic certain characteristics of real data while allowing researchers and developers to control variables, repeat experiments, and test systems under varied scenarios.

Types include numeric data, categorical data, time series, and spatial data. Generation methods range from random sampling from statistical distributions and Monte Carlo simulations to agent-based models, rule-based generators, procedural content generation, and modern generative models. Reproducibility is often achieved with fixed random seeds.

Common applications are software testing and benchmarking, development of machine learning and analytics workflows, privacy-preserving data synthesis, education and training, and scenario analysis for risk assessment or policy exploration.

Quality is assessed by comparing summary statistics and distributions to the target domain, using distance measures such as the Kolmogorov-Smirnov statistic or Wasserstein distance. Documentation of assumptions and provenance, along with reproducible seeds, supports traceability.

Practical tools include general-purpose programming environments with libraries for random data generation (for example, NumPy and Faker), data generation packages (such as simstudy or synthpop in specific ecosystems), and dedicated simulators like discrete-event or agent-based platforms.

Limitations include the risk that simulated data fails to capture rare events or complex dependencies, potential bias if the model is misspecified, and the danger of overfitting analyses to synthetic scenarios. Ethical and privacy considerations should guide data synthesis.