Year 10 Mathematics | Victorian Curriculum 2.0
Approximation and accuracy
Topic 01 | Number & Algebra | Practice

What you will learn

  • distinguish between exact and approximate representations of numbers,
  • understand how rounding and truncation introduce error,
  • calculate absolute and relative (percentage) error,
  • analyse how errors accumulate when approximate values are used in repeated calculations,
  • decide when an exact form (surd, fraction) is preferable to a decimal approximation.
Why does accuracy matter?

Every measurement is an approximation. When you round a number and then use that rounded value in further calculations, the error can grow — sometimes dramatically. Engineers designing bridges, pharmacists calculating drug doses, and programmers writing financial software all need to understand how small rounding errors can snowball into serious mistakes.

Where you'll see this
  • Finance: interest calculations repeated over many periods amplify rounding errors.
  • Science: experimental measurements are always approximate; results must state appropriate precision.
  • Engineering: tolerances in manufacturing depend on understanding accumulated error.
  • Computing: floating-point arithmetic means computers routinely deal with approximation.
Worked example 0 Real-world example: accumulated rounding in finance

A bank account earns 3.7% interest per year, compounded annually. The principal is $10 000. Compare the balance after 5 years using (a) the exact multiplier and (b) the multiplier rounded to 2 decimal places.

  1. Exact multiplier: 1.0371.0371.037. After 5 years: 10 000×1.0375=10 000×1.198 691…=11 986.9110\,000 \times 1.037^5 = 10\,000 \times 1.198\,691\ldots = 11\,986.9110000×1.0375=10000×1.198691…=11986.91 (to nearest cent).
  2. Rounded multiplier: 1.041.041.04. After 5 years: 10 000×1.045=10 000×1.216 653=12 166.5310\,000 \times 1.04^5 = 10\,000 \times 1.216\,653 = 12\,166.5310000×1.045=10000×1.216653=12166.53.
  3. The difference is 12 166.53−11 986.91=179.6212\,166.53 - 11\,986.91 = 179.6212166.53−11986.91=179.62 — a significant error from rounding just one decimal place.

Key idea: rounding before repeated multiplication compounds the error at every step.

1. Exact vs approximate representations

An exact representation leaves no ambiguity about the true value:

  • Fractions: 13\dfrac{1}{3}31​ is exact; 0.3330.3330.333 is an approximation.
  • Surds: 2\sqrt{2}2​ is exact; 1.4141.4141.414 is an approximation.
  • Multiples of π\piπ: 2π2\pi2π is exact; 6.2836.2836.283 is an approximation.

A decimal approximation is often more practical for measurement and communication, but it always introduces some error.

Worked example 1 Exact vs decimal form

Express 57\dfrac{5}{7}75​ as a decimal and state the error when rounded to 3 decimal places.

  1. 57=0.714 285 714…\dfrac{5}{7} = 0.714\,285\,714\ldots75​=0.714285714… (recurring).
  2. Rounded to 3 d.p.: 0.7140.7140.714.
  3. Error =0.714 285…−0.714=0.000 285…= 0.714\,285\ldots - 0.714 = 0.000\,285\ldots=0.714285…−0.714=0.000285…

The exact form 57\dfrac{5}{7}75​ carries no error at all.

2. Rounding and truncation

Rounding replaces a number with the nearest value at a given precision (e.g. to 2 decimal places, 3 significant figures).

Truncation simply cuts off digits beyond a given place — it always rounds toward zero.

Formula reference

Absolute error=∣approximate value−exact value∣\text{Absolute error} = |\text{approximate value} - \text{exact value}|Absolute error=∣approximate value−exact value∣Relative error=absolute error∣exact value∣×100%\text{Relative error} = \frac{\text{absolute error}}{|\text{exact value}|} \times 100\%Relative error=∣exact value∣absolute error​×100%
Worked example 2 Comparing rounding and truncation

The exact value is 3.64783.64783.6478. Find the absolute and relative error when this is (a) rounded to 2 d.p. and (b) truncated to 2 d.p.

  1. Rounded to 2 d.p.: 3.653.653.65. Absolute error =∣3.65−3.6478∣=0.0022= |3.65 - 3.6478| = 0.0022=∣3.65−3.6478∣=0.0022. Relative error =0.00223.6478×100%≈0.060%= \dfrac{0.0022}{3.6478} \times 100\% \approx 0.060\%=3.64780.0022​×100%≈0.060%.
  2. Truncated to 2 d.p.: 3.643.643.64. Absolute error =∣3.64−3.6478∣=0.0078= |3.64 - 3.6478| = 0.0078=∣3.64−3.6478∣=0.0078. Relative error =0.00783.6478×100%≈0.214%= \dfrac{0.0078}{3.6478} \times 100\% \approx 0.214\%=3.64780.0078​×100%≈0.214%.

Truncation produced a larger error in this case.

Worked example 3 Significant figures

Round 0.004 562 30.004\,562\,30.0045623 to 3 significant figures.

  1. The first three significant figures are 444, 555, and 666 (leading zeros are not significant).
  2. The next digit is 2<52 < 52<5, so round down.
  3. Result: 0.004 560.004\,560.00456.

3. Error accumulation in repeated calculations

When an approximate value is used repeatedly — for example, multiplied by itself — the error grows with each step.

Worked example 4 Error growth through repeated multiplication

A length is measured as x=4.3x = 4.3x=4.3 cm (rounded to 1 d.p., so the true value is between 4.254.254.25 and 4.354.354.35). Find the range of possible values for x3x^3x3.

  1. Minimum: 4.253=76.765 6254.25^3 = 76.765\,6254.253=76.765625.
  2. Maximum: 4.353=82.312 8754.35^3 = 82.312\,8754.353=82.312875.
  3. Using the rounded value: 4.33=79.5074.3^3 = 79.5074.33=79.507.
  4. The result could be anywhere from about 76.876.876.8 to 82.382.382.3 — a range of 5.55.55.5 cm3^33, even though the original measurement error was only ±0.05\pm 0.05±0.05 cm.

Key idea: cubing amplified the original ±0.05\pm 0.05±0.05 error into a range of 5.55.55.5 — over 100 times larger.

Worked example 5 Rounding at each step vs rounding at the end

Calculate 1.7×2.3×4.11.7 \times 2.3 \times 4.11.7×2.3×4.1 two ways: (a) round each intermediate product to 1 d.p., (b) keep full precision and round only the final answer.

  1. Method (a): 1.7×2.3=3.91≈3.91.7 \times 2.3 = 3.91 \approx 3.91.7×2.3=3.91≈3.9. Then 3.9×4.1=15.99≈16.03.9 \times 4.1 = 15.99 \approx 16.03.9×4.1=15.99≈16.0.
  2. Method (b): 1.7×2.3=3.911.7 \times 2.3 = 3.911.7×2.3=3.91. Then 3.91×4.1=16.0313.91 \times 4.1 = 16.0313.91×4.1=16.031. Rounded to 1 d.p.: 16.016.016.0.
  3. In this case the answers agree, but with longer calculation chains or less friendly numbers the early-rounding method can diverge significantly.

Key idea: always keep full precision through intermediate steps and round only the final answer.


Practice

Fluency

Tier 1: representations and basic error

    1. State whether each is exact or approximate: (a) 5\sqrt{5}5​ (b) 2.2362.2362.236 (c) 227\dfrac{22}{7}722​ (d) 3.143.143.14.
    2. Round 7.34967.34967.3496 to (a) 1 d.p., (b) 2 d.p., (c) 3 significant figures.
    3. Truncate 7.34967.34967.3496 to (a) 1 d.p., (b) 2 d.p.
    4. Find the absolute error when π\piπ is approximated by 3.143.143.14.
    5. Find the relative (percentage) error when 3\sqrt{3}3​ is approximated by 1.731.731.73.
    6. A calculator shows 0.666 666 70.666\,666\,70.6666667 for 23\dfrac{2}{3}32​. What is the absolute error?
    7. Round 0.005 0490.005\,0490.005049 to 2 significant figures.
    8. Express 56\dfrac{5}{6}65​ as a decimal rounded to 4 d.p. and state the absolute error.
    9. A length of 73\dfrac{7}{3}37​ m is recorded as 2.332.332.33 m. Find the absolute and relative error.
    10. True or false: truncation always gives a smaller error than rounding. Justify your answer.
Reasoning

Tier 2: accumulated error

    1. A square has side length 5.25.25.2 cm (rounded to 1 d.p.). Find the range of possible values for the area.
    2. An investment of $1000 grows by a factor of 1.0651.0651.065 each year. Compare the amount after 10 years using the exact multiplier vs the multiplier rounded to 1.071.071.07.
    3. A recipe calls for 23\dfrac{2}{3}32​ cup of sugar. A cook measures 0.670.670.67 cups. If the recipe is tripled, what is the total error?
    4. The radius of a circle is 3.43.43.4 cm (rounded to 1 d.p.). Find the range of possible values for the circumference. Use C=2πrC = 2\pi rC=2πr.
    5. A student calculates 2.45×3.15×1.852.45 \times 3.15 \times 1.852.45×3.15×1.85 by rounding each factor to 1 d.p. first. Find the error compared to the exact product.
    6. Explain why keeping values in surd form during intermediate steps gives a more accurate final answer.
    7. The value x=1.4x = 1.4x=1.4 is used to compute x5x^5x5. If the true value is 1.411.411.41, find the absolute error in x5x^5x5.
    8. A measurement of 12.012.012.0 cm has a relative error of 0.5%0.5\%0.5%. Find the range of possible true values.
Reasoning

Tier 3: analysis and explanation

    1. Prove that when a value rounded to ±ϵ\pm\epsilon±ϵ is squared, the maximum absolute error in the square is approximately 2xϵ2x\epsilon2xϵ (for small ϵ\epsilonϵ). Hint: compare (x+ϵ)2(x + \epsilon)^2(x+ϵ)2 with x2x^2x2.
    2. A GPS unit reports a distance of 142142142 km, rounded to the nearest kilometre. This distance is used to calculate fuel needed at 8.38.38.3 L per 100 km. Find the maximum error in the fuel estimate.
    3. A scientist measures the sides of a cuboid as 3.23.23.2 cm, 4.54.54.5 cm and 6.16.16.1 cm (each to 1 d.p.). Calculate the maximum and minimum possible volumes and the percentage range.
    4. Explain, with an example, why subtraction of nearly equal approximate numbers is particularly dangerous for accuracy.

Challenge

Reasoning

Harder reasoning

    1. A bank compounds interest monthly at a nominal rate of 4.8%4.8\%4.8% per annum. Compare the balance after 20 years on a $50 000 deposit using (a) the exact monthly multiplier 1+0.048121 + \dfrac{0.048}{12}1+120.048​ and (b) the multiplier rounded to 4 d.p. How large is the discrepancy?
    2. The golden ratio is ϕ=1+52\phi = \dfrac{1 + \sqrt{5}}{2}ϕ=21+5​​. A student approximates ϕ≈1.618\phi \approx 1.618ϕ≈1.618 and calculates ϕ10\phi^{10}ϕ10. Find the percentage error compared to the exact value of ϕ10\phi^{10}ϕ10.
    3. Two measurements are a=10.0±0.05a = 10.0 \pm 0.05a=10.0±0.05 and b=9.9±0.05b = 9.9 \pm 0.05b=9.9±0.05. Show that the relative error of a−ba - ba−b can exceed 100%100\%100% while the relative errors of aaa and bbb individually are each under 1%1\%1%.
    4. A computer stores numbers in floating point with 7 significant digits. Explain how computing 10 000 001−10 000 000\sqrt{10\,000\,001} - \sqrt{10\,000\,000}10000001​−10000000​ could lose almost all significant figures, and describe a rearrangement that avoids this problem.
Year 10 Mathematics study companion | Practice