{"id":843,"date":"2026-10-31T21:52:45","date_gmt":"2026-10-31T21:52:45","guid":{"rendered":"https:\/\/noesynthesis.org\/blog\/?p=843"},"modified":"2026-10-31T21:52:45","modified_gmt":"2026-10-31T21:52:45","slug":"precision-versus-accuracy","status":"publish","type":"post","link":"https:\/\/noesynthesis.org\/blog\/precision-versus-accuracy\/","title":{"rendered":"Precision vs Accuracy: Why Matching Measurements Can Still Mislead"},"content":{"rendered":"
\n

A cluster of matching results can show a stable method without showing a correct result.<\/h2>\n

Y<\/span>ou place the same object on a scale three times. Each reading is almost identical. It feels like the instrument has earned your trust. But suppose its zero setting is wrong. The agreement is real; so is the possibility that every reading is displaced in the same direction.<\/p>\n

This is a hypothetical example, not a test of a particular device. Its lesson is useful whenever a neat set of numbers seems to settle a question: agreement among measurements and agreement with what is being measured are different achievements.<\/p>\n

\"Conceptual
Original AI-assisted conceptual illustration by M.R. Orion. The clusters are schematic, not experimental data.<\/figcaption><\/figure>\n

What the cluster tells you<\/h2>\n

In its guidance on measurement terminology<\/a>, the US National Institute of Standards and Technology distinguishes accuracy from precision. Accuracy concerns closeness to the value of the quantity being measured. Precision concerns agreement among independent test results under stated conditions. Repeated readings under the same conditions can tell us about repeatability, but that does not make precision another word for accuracy.<\/p>\n

The conditions matter. Did the same person use the same instrument in the same room over a short interval? Or did different laboratories use different methods? A statement that results \u201cagree\u201d leaves out something important unless it explains what was held constant and what changed.<\/p>\n

Even the word independent deserves attention. Copying a number into three spreadsheets produces three entries, not three independent measurements. Taking fresh readings gives more information, but a shared instrument problem may still affect all of them.<\/p>\n

Why more readings may not fix the problem<\/h2>\n

Some effects make readings scatter. Others can shift them together. NIST distinguishes random and systematic error, while stressing that the error in a measurement is generally unknown. If an unchanged zero offset affects every weighing, collecting additional readings does not by itself identify or remove that offset.<\/p>\n

A comparison with an appropriate reference can help reveal a systematic effect. That comparison has its own conditions and uncertainty; it is not an encounter with a perfectly known truth. Applying a correction also does not erase every doubt. NIST notes that a systematic error cannot be known perfectly, so its compensation cannot be complete.<\/p>\n

The practical question changes from \u201cHow many times did it agree?\u201d to \u201cWhat could make all these results agree for the wrong reason?\u201d That question does not dismiss repeat measurements. It helps identify the next check that repetition alone cannot supply.<\/p>\n

A small spread is not the whole uncertainty<\/h2>\n

Measurement uncertainty describes more than the spread you happen to see in one series. Depending on the measurement, relevant contributions may involve the reference used for comparison, instrument resolution, environmental conditions and the estimated correction. A tight cluster is evidence about part of the process, not permission to ignore the rest.<\/p>\n

NIST also cautions against assigning a number directly to the qualitative word accuracy. Reporting an identified standard uncertainty is more informative than saying an instrument has \u201can accuracy\u201d of a certain amount without explaining what that amount represents. A useful report names the quantity, the units, the method and the uncertainty statement instead of relying on an impressive adjective.<\/p>\n

Ask what would change the decision<\/h2>\n

Imagine two measurements that differ slightly. Without their uncertainties and conditions, it may be premature to call one a contradiction of the other. Conversely, two nearly identical results may share the same limitation. Neither agreement nor disagreement interprets itself.<\/p>\n

Before using a number, ask what was measured, how the result was checked against a reference, and which uncertainty contributions were considered. Then ask whether those limits matter for the intended decision. A measurement adequate for roughly sorting materials may be inadequate for a tighter engineering tolerance. This is a question about purpose, not a contest to display the most decimal places.<\/p>\n

Our earlier discussion of reproducibility and replicability<\/a> asks what kind of check a repeated scientific result represents. Here the narrower issue is the measurement itself: repeated agreement is valuable, but it cannot carry every conclusion we place on it.<\/p>\n

The next time a row of matching numbers looks reassuring, keep the reassurance proportional to the test. A method that repeats well has told you something. Ask what it has not yet had a chance to tell you.<\/p>\n

Source note: This explainer uses the terminology discussion in NIST Technical Note 1297, Appendix D.1 (1994 edition). It is not a claim that an older guidance document is a newly released standard. Examples are illustrative, not experimental findings.<\/em><\/p>\n

M.R. Orion<\/strong><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Repeated agreement is valuable, but it cannot rule out a shared measurement error. A practical guide to precision, accuracy and uncertainty.<\/p>\n","protected":false},"author":1,"featured_media":844,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"footnotes":""},"categories":[117,41],"tags":[98,161,152],"class_list":["post-843","post","type-post","status-publish","format-standard","has-post-thumbnail","category-editorial-en","category-technical-outreach","tag-critical-thinking","tag-measurement","tag-uncertainty"],"acf":[],"yoast_head":"\nPrecision vs Accuracy: Why Matching Results Can Mislead<\/title>\n<meta name=\"description\" content=\"Matching measurements can share the same error. 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