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# Measurement variation

- Concept

Measuring the same thing twice almost never gives the same result: the person changes, the observer changes and the device changes.

Updated Nov 13, 2026

## Key points

- Within-subject: the person changes (time of day, posture, meals, stress); for example, blood pressure.
- Intra-observer: the same rater gives different results when repeating the reading.
- Inter-observer: different raters disagree; measured with kappa (categories) or the ICC (continuous values).
- Averaging n measurements divides random error by the square root of n; it does not fix systematic bias.
- Strategies: standard protocol, training, calibration, blinded reading, several measurements and quality control.

## What it is

Every clinical measurement varies for several reasons: the person's biological variation from one moment to the next (within-subject), the same observer repeating a reading (intra-observer), different observers (inter-observer) and the instrument. Knowing these sources helps decide how many measurements to take, how to standardise them and when a change between two visits is real. This page covers the sources of variation and how to reduce them; the difference between validity and reliability has its own page.

## Worked example

- A patient's systolic pressure: the standard deviation between readings in the same person is 6 mmHg.
- With a single reading, 95% of values fall within ± 1.96 × 6 ≈ ± 12 mmHg of the usual value.
- With the mean of 3 readings: 6 / √3 ≈ 3.5 mmHg; interval ± 1.96 × 3.5 ≈ ± 7 mmHg.
- With 4 readings: 6 / √4 = 3 mmHg; interval ± 5.9 mmHg.
- A reading of 146 mmHg with a usual value of 136 fits within the variation of a single measurement.
- Reading: averaging several readings cuts the noise nearly in half; deciding on one reading can misclassify.

## Sources of variation and how to reduce them

| Source | Example | How to reduce it |
| --- | --- | --- |
| Within-subject (biological) | Blood pressure changing during the day | Several measurements, same time and conditions |
| Intra-observer | Same radiologist, different reading another day | Written criteria, reading blind to the previous one |
| Inter-observer | Two doctors hear different sounds | Joint training, operational definitions, measure kappa or ICC |
| Instrument | An uncalibrated blood pressure monitor | Regular calibration against a standard |

## Markers

- Intra-observer
- Inter-observer
- Kappa
- ICC

## In practice

- Before diagnosing on a single value (blood pressure, glucose, spirometry), confirm it with repeated measurements.
- A change between two visits is real only if it exceeds the expected measurement variation.
- In studies, random error in the exposure usually weakens the association toward the null.

## Common pitfalls

- Believing that averaging fixes an uncalibrated device: it only reduces random error.
- Comparing studies that measured with different protocols.
- Not reporting the reliability (kappa, ICC) of the main measurements.

## Memory aid

Patient, doctor and device: three voices that never sing the same twice; to hear the tune, repeat and average.

## Test yourself

Think of the answer, then tap the question to see it.

## Within-subject SD of 6 mmHg. What is the standard error of the mean of 4 readings?

6 / √4 = 3 mmHg.

## Which type of error does averaging reduce, and which does it not?

It reduces random error; it does not fix systematic error (bias), which needs calibration.

## How is inter-observer agreement measured for a categorical variable?

With the kappa statistic, which corrects for agreement expected by chance.

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## Connections

## Related 6

- [Validity and reliability](https://durgical.com/en/epidemiology/validity-and-reliability/)
- [Agreement and the kappa statistic](https://durgical.com/en/epidemiology/agreement-and-the-kappa-statistic/)
- [Agreement for continuous measures: ICC and Bland-Altman](https://durgical.com/en/epidemiology/agreement-for-continuous-measures-icc-and-bland-altman/)
- [Information (measurement) bias](https://durgical.com/en/epidemiology/information-bias/)
- [Standard error](https://durgical.com/en/epidemiology/standard-error/)
- [Choosing and defining variables](https://durgical.com/en/epidemiology/choosing-and-defining-variables/)

[← All of epidemiology](https://durgical.com/en/epidemiology/)

Educational content on epidemiological and statistical methods; the examples use made-up numbers to teach the calculation. Not a clinical or legal guide: current Colombian rules are in the official sources.

Sources: [2.1.1.4. Variability](https://www.itl.nist.gov/div898/handbook/mpc/section1/mpc114.htm) (NIST/SEMATECH e-Handbook of Statistical Methods, 2012); [2.1.1.3. Bias and Accuracy](https://www.itl.nist.gov/div898/handbook/mpc/section1/mpc113.htm) (NIST/SEMATECH e-Handbook of Statistical Methods, 2012); [2.4. Gauge R & R studies](https://www.itl.nist.gov/div898/handbook/mpc/section4/mpc4.htm) (NIST/SEMATECH e-Handbook of Statistical Methods, 2012).

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Representative model, for educational purposes only.

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License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Cite as: DurgiPedia (durgical.com), https://durgical.com/en/epidemiology/measurement-variation/