Measurement deviations arise from different mechanisms, and repeated measurements do not treat all of them the same way.

A random effect changes unpredictably between observations under nominally identical conditions. Electrical noise, finite counting statistics, or small uncontrolled fluctuations can make repeated readings scatter. Averaging independent random fluctuations often reduces their contribution to uncertainty.

A systematic effect shifts measurements in a repeatable or structured way. A zero offset, an incorrect scale factor, thermal drift, parallax from a fixed viewing geometry, or using an incomplete measurement model can bias every observation similarly. Repeating the same procedure does not automatically remove such a bias.

Suppose a balance has a $+2,\mathrm{g}$ zero offset and each reading also fluctuates randomly by about $0.5,\mathrm{g}$. Measuring the same object many times can determine the mean reading very precisely, but the mean remains about $2,\mathrm{g}$ too high unless the offset is corrected or included in the model.

The distinction is about the effect and procedure, not a permanent label attached to a device. Temperature variation might behave randomly during one experiment but create a systematic trend in another. Some effects can be diagnosed by reversing an apparatus, changing measurement order, comparing independent methods, or measuring a reference with known value.

Good experiments therefore do more than collect many repetitions. They ask what variables can move the result, which effects repetition can average down, which require calibration or redesign, and which remain as uncertainty in the reported result.