An Entity of Type: Thing, from Named Graph: http://dbpedia.org, within Data Space: dbpedia.org

Significance arithmetic is a set of rules (sometimes called significant figure rules) for approximating the propagation of uncertainty in scientific or statistical calculations. These rules can be used to find the appropriate number of significant figures to use to represent the result of a calculation. If a calculation is done without analysis of the uncertainty involved, a result that is written with too many significant figures can be taken to imply a higher precision than is known, and a result that is written with too few significant figures results in an avoidable loss of precision. Understanding these rules requires a good understanding of the concept of significant and insignificant figures.

Property Value
dbo:abstract
  • Significance arithmetic is a set of rules (sometimes called significant figure rules) for approximating the propagation of uncertainty in scientific or statistical calculations. These rules can be used to find the appropriate number of significant figures to use to represent the result of a calculation. If a calculation is done without analysis of the uncertainty involved, a result that is written with too many significant figures can be taken to imply a higher precision than is known, and a result that is written with too few significant figures results in an avoidable loss of precision. Understanding these rules requires a good understanding of the concept of significant and insignificant figures. The rules of significance arithmetic are an approximation based on statistical rules for dealing with probability distributions. See the article on propagation of uncertainty for these more advanced and precise rules. Significance arithmetic rules rely on the assumption that the number of significant figures in the operands gives accurate information about the uncertainty of the operands and hence the uncertainty of the result. For alternatives see Interval arithmetic and Floating-point error mitigation. An important caveat is that significant figures apply only to measured values. Values known to be exact should be ignored for determining the number of significant figures that belong in the result. Examples of such values include: * integer counts (e.g. the number of oranges in a bag) * definitions of one unit in terms of another (e.g. a minute is 60 seconds) * actual prices asked or offered, and quantities given in requirement specifications * legally defined conversions, such as international currency exchange * scalar operations, such as "tripling" or "halving" * mathematical constants, such as π and e Physical constants such as the gravitational constant, however, have a limited number of significant digits, because these constants are known to us only by measurement. On the other hand, c (the speed of light) is exactly 299,792,458 m/s by definition. (en)
dbo:wikiPageExternalLink
dbo:wikiPageID
  • 1237823 (xsd:integer)
dbo:wikiPageLength
  • 17216 (xsd:nonNegativeInteger)
dbo:wikiPageRevisionID
  • 1030203057 (xsd:integer)
dbo:wikiPageWikiLink
dbp:wikiPageUsesTemplate
dcterms:subject
gold:hypernym
rdfs:comment
  • Significance arithmetic is a set of rules (sometimes called significant figure rules) for approximating the propagation of uncertainty in scientific or statistical calculations. These rules can be used to find the appropriate number of significant figures to use to represent the result of a calculation. If a calculation is done without analysis of the uncertainty involved, a result that is written with too many significant figures can be taken to imply a higher precision than is known, and a result that is written with too few significant figures results in an avoidable loss of precision. Understanding these rules requires a good understanding of the concept of significant and insignificant figures. (en)
rdfs:label
  • Significance arithmetic (en)
owl:sameAs
prov:wasDerivedFrom
foaf:isPrimaryTopicOf
is dbo:wikiPageWikiLink of
is rdfs:seeAlso of
is foaf:primaryTopic of
Powered by OpenLink Virtuoso    This material is Open Knowledge     W3C Semantic Web Technology     This material is Open Knowledge    Valid XHTML + RDFa
This content was extracted from Wikipedia and is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License