Measurement science: which statements correctly describe systematic errors in observations and how they affect results?

Introduction / Context:
Errors in measurement are broadly classified as systematic, random, and blunders. Systematic errors are particularly important because they bias results in a predictable way and must be identified and corrected in surveying, astronomy, geodesy, and engineering metrology.

Given Data / Assumptions:

• Well-calibrated instruments may still have residual systematic effects.
• Environmental influences (temperature, refraction) may introduce predictable biases.
• Observer biases (e.g., reaction time) can be modeled.

Concept / Approach:
Systematic errors have determinate causes and generally obey known functional forms (e.g., scale error, zero error). Because they are predictable, we can apply corrections to observations to remove their effect. These errors tend to bias results consistently high or low, hence are often termed cumulative.

Step-by-Step Solution:
Identify determinism: systematic errors follow laws (temperature coefficient, index error, etc.).Apply corrections: once quantified, correct observations to mitigate bias.Bias direction: results skew consistently in one direction.Terminology: cumulative errors is a common synonym in classical texts.

Verification / Alternative check:
Instrument calibration certificates provide correction tables precisely to handle systematic components, confirming their determinate nature.

Why Other Options Are Wrong:
Here, none are wrong; all characterize systematic errors accurately, so the aggregate choice is correct.

Common Pitfalls:
Confusing random scatter (handled statistically) with systematic bias; neglecting periodic errors (a subset with sine/cosine forms) that are still systematic.

Final Answer:
All the above