(mathematics) A prescribed decimal place which determines the amount of rounding off to be done; this is usually based upon the degree of accuracy in measurement. Also known as significant digit.
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(mathematics) A prescribed decimal place which determines the amount of rounding off to be done; this is usually based upon the degree of accuracy in measurement. Also known as significant digit.
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| Sci-Tech Encyclopedia: Significant figures |
Digits that show the number of units in a measurement expressed in decimal notation.
Scientific notation is useful in showing which digits are significant. In scientific notation, a number is expressed as the product of a number 1 to 10 and a power of 10, or the product of 1 and a power of 10. Thus, the number 123,000 is 1.23 × 105.
The precision of a measurement is based on the size of the unit of measurement. The smaller the unit, the more precise is the measurement.
Computations cannot improve the precision of the measurement. To add measures, they should all be rounded to the unit of the least precise measurement. The sum 8.6 cm + 0.14 cm + 2.75 cm is found by rounding each to tenths: 8.6 cm + 0.1 cm + 2.8 cm = 11.5 cm. Even by doing this, the absolute error might be as large as 0.05 + 0.005 + 0.005 or 0.06, and affect the result by as much as 0.1.
In multiplying and dividing approximate numbers, the product or quotient is rounded to the number of significant digits in the number with the fewest significant digits. For example, 6.2 m × 8.75 m by computation is 54.25. The product needs to be rounded to 54 m2 so as to show two significant digits. See also
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This article may be confusing or unclear to readers. Please help clarify the article; suggestions may be found on the talk page. (September 2009) |
The significant figures (also called significant digits and abbreviated sig figs, sign.figs or sig digs) of a number are those digits that carry meaning contributing to its precision (see entry for Accuracy and precision). This includes all digits except:
The concept of significant figures is often used in connection with rounding. Rounding to n significant figures is a more general-purpose technique than rounding to n decimal places, since it handles numbers of different scales in a uniform way. A practical calculation that uses any irrational number necessitates rounding the number, and hence the answer, to a finite number of significant figures. Computer representations of floating point numbers typically use a form of rounding to significant figures, but with binary numbers.
The term "significant figures" can also refer to a crude form of error representation based around significant figure rounding; for this use, see Significance arithmetic.
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The rules for identifying significant digits when writing or interpreting numbers are as follows:
has three significant figures (and hence indicates that the number is accurate to the nearest ten).A number with all zero digits (e.g. 0.000) has no significant digits, because the uncertainty is larger than the actual measurement.
Generally, the same rules apply to numbers expressed in scientific notation. However, in the normalized form of that notation, placeholder leading and trailing digits do not occur, so all digits are significant. For example, 0.00012 (two significant figures) becomes 1.2×10−4, and 0.000122300 (six significant figures) becomes 1.22300×10−4. In particular, the potential ambiguity about the significance of trailing zeros is eliminated. For example, 1300 to four significant figures is written as 1.300×103, while 1300 to two significant figures is written as 1.3×10 3.
To round to n significant figures:
For multiplication and division, the result should have as many significant figures as the measured number with the smallest number of significant figures.
For addition and subtraction, the result should have as many decimal places as the measured number with the smallest number of decimal places.
If a sprinter is measured to have completed a 100-metre race in 11.71 seconds, what is the sprinter's average speed? By dividing the distance by the time using a calculator, we get a speed of 8.53970965 m/s.
The most straightforward way to indicate the precision of this result (or any result) is to state the uncertainty separately and explicitly, for example in the above case as 8.5397±0.0037 m/s or equivalently 8.5397(37) m/s. This is particularly appropriate when the uncertainty itself is important and precisely known (here, 100 m is presumed to be precise, and the time is 11.71±0.005 s, or an uncertainty of nearly 430 ppm). In this case, it is safe and indeed advantageous to provide more digits than would be called for by the significant-figures rules.
If the degree of precision in the answer is not important, it is again safe to express trailing digits that are not known exactly, for example 8.5397 m/s.
If, however, we are forced to apply significant-figures rules, expressing the result as 8.53970965 m/s would seem to imply that the speed is known to the nearest 10 nm/s or thereabouts, which would improperly overstate the precision of the measurement. Reporting the result using three significant figures (8.54 m/s) might be interpreted as implying that the speed is somewhere between 8.535 and 8.545 m/s. This is actually very close to the true precision, the actual speed being somewhere between 8.5360 and 8.5434 m/s. Reporting the result using two significant figures (8.5 m/s) would introduce considerable roundoff error and degrade the precision of the result.
(Note: in actual practice, 100 m is not this precise! For example, a pair of 0.05-metre-wide (2-inch) lines at the start and end would introduce a separate uncertainty of ±0.05 m (2 in) or 500 ppm to the above calculation. Now the total uncertainty has risen to 500 + 430 = 930 ppm, since both sources must be added together. Applied to the speed, that now becomes 8.5397±0.0080 or 8.5397(80) m/s, the actual speed being somewhere between 8.5317 and 8.5477 m/s.)
Numbers are often rounded off to make them easier to read. It's easier for someone to compare (say) 18% to 36% than to compare 18.148% to 35.922%. Similarly, when reviewing a budget, a series of figures like:
Division A: $185 000 Division B: $ 45 000 Division C: $ 67 000
is easier to understand and compare than a series like:
Division A: $184 982 Division B: $ 44 689 Division C: $ 67 422
To reduce ambiguity, such data are sometimes represented to the nearest order of magnitude, like:
Revenue (in thousands of dollars): Division A: 185 Division B: 45 Division C: 67
People who are not experts in metrology or statistics can overestimate the usefulness of significant figures. The topic receives much more emphasis in high-school and undergraduate chemistry texts[2] [1] than it does in real-world research laboratories.[3] [4]
Practicing scientists commonly express uncertain quantities in the form 1.23±0.06 or equivalently 1.23(6). The benefit is that the nominal value of the quantity is expressed by one numeral (1.23) while the uncertainty is expressed by a separate numeral (0.06). Expressing these two things explicitly and separately is more sensible than trying to encode both the nominal value and the uncertainty into a single numeral, where the uncertainty range is constrained to being a power of ten.
"Significant figures" primarily refers to a type of rounding, and is arguably appropriate when roundoff of the final answer is the dominant contribution to the uncertainty. However, there are many important situations where roundoff of the final answer is not the dominant contribution to the uncertainty. Indeed, in experimental research (especially metrology), only in a very badly designed experiment would such roundoff error be dominant, because roundoff errors are so easily reduced. Furthermore, even when roundoff error is dominant, it is preferable to indicate this explicitly, as in 1.24(½) or equivalently 1.24(⁄).
Secondarily, "significant figures" may refer to a crude scheme for significance arithmetic, but as discussed in the significance arithmetic article and elsewhere,[5] there is generally not any rigorous way to express the uncertainty using significant figures.
In computer science and numerical analysis, good practice demands the use of guard digits. This is incompatible with any notion of significant figures. For a discussion, see Acton.[6]
Good examples of how real scientists express uncertain quantities can be found in the NIST compendium of physical constants.[3] None of the values there conform to any "significant figures" rules.
Procedures for how to properly represent uncertainty, and the rationale for these procedures, can be found in the references.[5] [4]
In binary notation, it is common to refer to most or least significant digits to refer to the left-most or right-most digits in the binary string. For example in the binary string: 1010 1111, the most significant are 1010 and the least significant are 1111.
This entry is from Wikipedia, the leading user-contributed encyclopedia. It may not have been reviewed by professional editors (see full disclaimer)
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Some good "Significant figures" pages on the web:
Math mathworld.wolfram.com |
| precision | |
| significant digits | |
| significant |
| Why are significant figures used and how are significant figures obtained? | |
| What are the rules in finding significant figures? | |
| How many significant figures does 28.0 have? |
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