Science // Scientific Inquiry

Repeated tests for reliability

Distinguish between precision (consistency of repeated readings) and accuracy (closeness to true value), use significant figures and standard form correctly, and choose and use appropriate measuring instruments to minimise uncertainty

Ages
11 to 12
Type
Concept
Builds on
1 topic
Leads to
1 topic
Standards
3 codes

Quick check

If the learner weighed the same stone five times and got very similar answers but they were all slightly too high, could they explain the difference between being precise and being accurate — and why having lots of repeats doesn’t always fix the problem?

Ask this to see whether the topic is secure.

Topic // Mastery

What mastery looks like

What a learner can do once this topic is secure.
  • Explains the distinction between precision and accuracy with examples
  • Rounds measurements to an appropriate number of significant figures
  • Selects a measuring instrument with appropriate resolution for the context (e.g. choosing a 10 ml measuring cylinder rather than a 1-litre measuring jug for a 5 ml measurement)

Topic // Map

Where this fits

What to learn first and what this topic opens up, with the reason for each link. Required links come first.
  • RequiredMust come first
  • HelpfulHelps, but not essential

Before this

1 topic
  • Accurate MeasurementRequired

    Scientific Inquiry // Ages 9 to 11

    Precision vs accuracy and significant figures build on KS2 experience of taking careful measurements with repeat readings

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Repeated tests for reliability

Ages 11 to 12 // Concept

Leads to

1 topic
  • Tables, charts, and graphsHelpful

    Scientific Inquiry // Ages 12 to 13

    Correct use of SI units and significant figures in tables and axes is grounded in the precision/accuracy topic

Topic // Research-informed

Teaching notes

From the Marble Skill Taxonomy. These notes have not been validated in classrooms, so treat them as a starting point for your own judgement.

The approach

Help 11–12 year-olds separate PRECISION (how tightly repeated readings agree with each other) from ACCURACY (how close readings are to the true value), recognising the two are independent so data can be precise-but-inaccurate, accurate-but-imprecise, both, or neither. Learners record measurements to a justified number of significant figures (no false precision) and use standard form where sensible, choose instruments whose resolution suits the quantity, and understand that averaging repeated readings reduces the effect of random error on the mean but cannot cure a systematic bias.

Common misconceptions

  • Using 'precise' and 'accurate' as synonyms.
  • Believing tightly-agreeing (precise) readings must be correct, ignoring a systematic offset.
  • Thinking taking more repeats can fix an inaccurate (biased) instrument — repeats reduce spread, not bias.
  • Quoting far more significant figures than the instrument's resolution justifies (false precision).
  • Assuming a bigger or more expensive instrument is automatically more precise, regardless of resolution for the quantity.
  • Treating a single close reading as proof of accuracy without knowing the true value or the spread.

Useful representations

  • Target/bullseye model — cluster spread = precision, cluster offset = accuracy
  • Four-quadrant grid of precise/imprecise × accurate/inaccurate outcomes
  • Significant-figures ladder tied to instrument resolution
  • Instrument-resolution comparison (1-litre jug vs 10 ml cylinder for a 5 ml volume)
  • Repeat-readings dot-plot showing spread (precision) around a mean, with the true value marked (accuracy)

Teaching practices

  • Use the target/dartboard model to separate the two axes: spread of the cluster = precision; offset of the cluster from the bullseye = accuracy.
  • Deliberately teach all four cases — precise+accurate, precise+inaccurate, imprecise+accurate, imprecise+inaccurate — so pupils see the two ideas are independent.
  • Connect precision to the instrument's resolution and reliability to the number of repeats — different levers on data quality.
  • Teach significant figures as 'don't claim more certainty than your instrument gives', linking the number of sig figs to the resolution.
  • Practise instrument selection by resolution: match the finest sensible instrument to the size of the quantity and justify it.
  • Distinguish random error (reduced by repeats/averaging, shows as spread) from a bias that shifts accuracy (not fixed by averaging) — previewing the KS3 error-analysis topic.

Activities to try

  1. 1

    Two Dials target range: pulling precision and accuracy apart

    Why it works: Precision and accuracy are two independent properties of a data set: spread of the cluster shows precision, offset of the cluster from the true value shows accuracy. Taking more repeats does not tighten the spread of individual readings, but averaging them reduces the effect of random error on the mean; it can never remove a systematic bias.

    1. Face a bullseye target with two separate controls: a SPREAD dial and an OFFSET (bias) dial.
    2. Fire ten shots with spread set high and observe that the shots scatter all over the target.
    3. Turn the spread dial down and watch the shots pull into a tight knot — name this as high precision.
    4. With the knot tight and near centre, nudge the OFFSET dial and watch the whole tight cluster slide off to one side (e.g. top-left) — still tight, but far from the bullseye.
    5. State what you now see: the shots are as consistent as ever (precise) yet completely off-target (inaccurate), so one dial cannot fix the other.
  2. 2

    Four targets reference: labelling the four cases

    Why it works: Each of the four precision/accuracy combinations can be read directly off a target: spread tells you precision, position of the cluster relative to the centre tells you accuracy.

    1. Study four bullseye diagrams, one for each combination: precise+accurate (tight on the bullseye), precise+inaccurate (tight but off-centre), imprecise+accurate (scattered but centred on the bullseye), imprecise+inaccurate (scattered and off-centre).
    2. For each target, state in your own words why it fits that label before revealing the explanation.
    3. Reveal a lift-the-flap explanation on each target (e.g. 'precise + inaccurate: tight but off-centre — why?') and compare it to your reasoning.
    4. Use a linked panel that connects spread → random error (affected by instrument resolution and technique), number of repeats → how reliably the average is pinned down, and offset → systematic bias.
  3. 3

    Classify real repeated-reading sets

    Why it works: Amir is precise but not accurate (readings close to each other but all ~5 g from 100.0 — a systematic error); Bea is accurate on average but not precise (wide scatter averaging near 100.0); Cara is both. Precision is agreement of repeats; accuracy is closeness to the true value; significant figures reflect instrument resolution, not accuracy.

    1. Read three students' five repeated weighings of the same 100.0 g mass: Amir gets 95.1, 95.0, 95.2, 95.1, 95.0 g; Bea gets 100.3, 99.6, 100.4, 99.7, 100.0 g; Cara gets 100.1, 100.0, 99.9, 100.0, 100.1 g.
    2. Decide whose readings are precise, whose are accurate, and whose are both, and write the reason for each.
    3. Explain in your own words what 'precise' and 'accurate' each mean, using these results as evidence.
    4. Explain why Amir's readings are the tricky case — consistent yet consistently wrong.
    5. Answer: what could make Amir's readings consistently 5 g low, and would taking more repeats fix it?

Topic // Standards

Curriculum standards

Codes for the curriculum statements this topic maps to. Each publisher owns its standard text.
The national curriculum in England: Key stages 1 and 2 framework document
KS3.Sci.WS.SA.5KS3.Sci.WS.VUSN.1KS3.Sci.WS.VUSN.2

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