expectedresults

Expected results are predefined outcomes that an experiment, test, model, or process should produce under specified conditions. They serve as a reference against which observed outcomes are evaluated. In scientific research, expected results follow from the hypothesis and experimental design and are stated in measurable terms to enable statistical comparison with actual data.

In software testing, expected results specify the output, state, or behavior for given inputs. They are encoded as assertions in test cases and used to determine whether a test passes. In data analysis and machine learning, expected results may include performance metrics such as accuracy, latency, or error rates, guiding evaluation and model selection.

Defining expected results involves clarifying input conditions, environment, data versions, timing, units, and tolerance for variability. They should be explicit, reproducible, and checkable, allowing different observers to reproduce the assessment. They must distinguish between exact equality and acceptable tolerances, especially in systems with inherent variability or floating-point calculations.

Comparison between actual and expected results drives decision-making. A match supports a successful replication, validation, or release, while a mismatch prompts investigation into possible errors, experiment design issues, data quality problems, or model degradation. Documenting the expected results and the criteria for evaluation is essential for transparency and auditability.

Best practices include defining expected results before execution, using clear and testable criteria, versioning the expectations, and accounting for edge cases and non-deterministic behavior. Common pitfalls include vague or shifting expectations, ambiguous tolerances, and failing to align expectations with real-world requirements. Properly defined expected results enhance reliability, reproducibility, and user confidence in the findings.