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Orders of magnitude (numbers)

Orders of magnitude (numbers) is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Orders of magnitude (numbers) rather than just read about it. In short: This list contains selected positive numbers in increasing order of magnitude, including counts of things, dimensionless quantities, and probabilities. Each number is given a name in the short scale, which is used in English-speaking countries, as well as a name in the long scale, which is used in some of the countries that do not have English as their national language.

Orders of magnitude (numbers) — main illustration
Orders of magnitude (numbers) — illustration

Key takeaways

  • Orders of magnitude (numbers) belongs to mathematics; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Orders of magnitude (numbers) to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Orders of magnitude (numbers) from memory before moving on to harder problems.

Reference excerpt

This list contains selected positive numbers in increasing order of magnitude, including counts of things, dimensionless quantities, and probabilities. Each number is given a name in the short scale, which is used in English-speaking countries, as well as a name in the long scale, which is used in some of the countries that do not have English as their national language.

Smaller than 10−100 (one googolth)

Mathematics: The difference between 3 and the next smallest fusible number is less than 1 / ( 2 ↑ 9 16 ) {\displaystyle 1/(2\uparrow ^{9}16)} . Mathematics: 2−1541023937 ≈ 8.4489×10−463894430 is the difference between 3 and the next smallest tame fusible number as defined by Erickson et al. (2021). Mathematics: 10−183800 is the approximate probability that a typing "monkey", or an English-illiterate typing robot, when placed in front of a typewriter with no spaces or punctuation, will type out William Shakespeare's play Hamlet on the first try. Computing: 2−262378 ≈ 2.24800708647703657297018614776265182597360918266100276294348974547709294462×10−78984 is the smallest non-zero value that can be represented by an octuple-precision IEEE floating-point value. Computing: 2−262142 ≈ 2.48242795146434978829932822291387172367768770607964686927095329791378756×10−78913 is the smallest positive normal number that can be represented by an octuple-precision IEEE floating-point value. Computing: 1×10−6176 is the smallest non-zero value that can be represented by a quadruple-precision IEEE decimal floating-point value. Computing: 1×10−6143 is the smallest positive normal number that can be represented by a quadruple-precision IEEE decimal floating-point value. Computing: 2−16494 ≈ 6.4751751194380251109244389582276466×10−4966 is the smallest non-zero value that can be represented by a quadruple-precision IEEE floating-point value. Computing: 2−16445 ≈ 3.6451995318824746025×10−4951 is the smallest non-zero value that can be represented by an 80-bit x86 double-extended IEEE floating-point value. Computing: 2−16382 ≈ 3.3621031431120935062626778173217526×10−4932 is the smallest positive normal number that can be represented by a quadruple-precision IEEE floating-point value and an 80-bit x86 double-extended IEEE floating-point value. Computing: 1×10−398 is the smallest non-zero value that can be represented by a double-precision IEEE decimal floating-point value. Computing: 1×10−383 is the smallest positive normal number that can be represented by a double-precision IEEE decimal floating-point value. Computing: 2−1074 ≈ 4.9406564584124654×10−324 is the smallest non-zero value that can be represented by a double-precision IEEE floating-point value. Computing: 2−1022 ≈ 2.2250738585072014×10−308 is the smallest positive normal number that can be represented by a double-precision IEEE floating-point value. Mathematics: 365 ! / 365 365 {\displaystyle 365!/365^{365}} ≈ 1.45×10−157 is the probability that in a randomly selected group of 365 people, all of them will have different birthdays. Computing: 1×10−101 is the smallest non-zero value that can be represented by a single-precision IEEE decimal floating-point value.

10−100 to 10−30

Computing: 1×10−95 is equal to the smallest positive normal number that can be represented by a single-precision IEEE decimal floating-point value. Mathematics: ⁠1/52!⁠ ≈ 1.24×10−68 is the probability of shuffling a standard 52-card deck in any specific order. Computing: 2−149 ≈ 1.4012985×10−45 is the smallest positive non-zero value that can be represented by a single-precision IEEE floating-point value. Computing: 2−126 ≈ 1.1754944×10−38 is the smallest positive normal number that can be represented by a single-precision IEEE floating-point value.

10−30 (0.000000000000000000000000000001; 1000−10; short scale: one nonillionth; long scale: one quintillionth) ISO: quecto- (q)

Mathematics: 4 ! × ( 13 ! ) 4 / 52 ! {\displaystyle 4!\times (13!)^{4}/52!} ≈ 4.47×10−28 is the approximate probability in a game of bridge of all four players getting a complete suit each.

10−27 (0.000000000000000000000000001; 1000−9; short scale: one octillionth; long scale: one quadrilliardth) ISO: ronto- (r)

10−24 (0.000000000000000000000001; 1000−8; short scale: one septillionth; long scale: one quadrillionth) ISO: yocto- (y)

10−21 (0.000000000000000000001; 1000−7; short scale: one sextillionth; long scale: one trilliardth) ISO: zepto- (z)

Mathematics: 1 / ( 80 20 ) {\displaystyle 1/{\binom {80}{20}}} ≈ 2.83×10−19 is the probability of matching 20 numbers for 20 in a game of keno. Mathematics: 2−63 ≈ 1.08×10−19 is the odds of a perfect bracket in the NCAA Division I men's basketball tournament if coin flips of 50/50 are used to predict the winners of the 63 matches.

10−18

(0.000000000000000001; 1000−6; short scale: one quintillionth; long scale: one trillionth) ISO: atto- (a)

Mathematics: 36−10 ≈ 2.74×10−16 is the probability of rolling snake eyes 10 times in a row on a pair of fair dice.

10−15 (0.000000000000001; 1000−5; short scale: one quadrillionth; long scale: one billiardth) ISO: femto- (f)

Mathematics: The Ramanujan constant, e π 163 = 262 537 412 640 768 743.999 999 999 999 25 … , {\displaystyle e^{\pi {\sqrt {163}}}=262\,537\,412\,640\,768\,743.999\,999\,999\,999\,25\ldots ,} is an almost integer, differing from the nearest integer by approximately 7.5×10−13.

… excerpt ends here. Continue reading the full article.

Illustrations

Orders of magnitude (numbers): Chimpanzee probably not typing Hamlet
Chimpanzee probably not typing Hamlet
Orders of magnitude (numbers): 1/52! chance of a specific shuffle
1/52! chance of a specific shuffle
Orders of magnitude (numbers): Snake eyes
Snake eyes
Orders of magnitude (numbers): Poker hands
Poker hands
Orders of magnitude (numbers): Eight planets of the Solar System
Eight planets of the Solar System

Worked examples

Example 1 — a first encounter with Orders of magnitude (numbers)

Start with the simplest possible case. Write down what Orders of magnitude (numbers) claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Orders of magnitude (numbers) before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Orders of magnitude (numbers) ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Orders of magnitude (numbers)

In research
Orders of magnitude (numbers) appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Orders of magnitude (numbers) in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Orders of magnitude (numbers) is common in secondary-school and first-year university syllabi. It links to neighbouring topics Orders of magnitude, so understanding it makes those chapters shorter.
In everyday life
Look for Orders of magnitude (numbers) outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study Orders of magnitude (numbers) in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Orders of magnitude (numbers) means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Orders of magnitude (numbers) out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Orders of magnitude (numbers) in simple terms?

This list contains selected positive numbers in increasing order of magnitude, including counts of things, dimensionless quantities, and probabilities. Each number is given a name in the short scale, which is used in English-speaking countries, as well as a name in the long scale, which is used in…

Why does Orders of magnitude (numbers) matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Orders of magnitude (numbers)?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Orders of magnitude (numbers).

Tags

  • Orders of magnitude

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