Higher: interpreting spread and comparing data
Higher: interpret spread, compare centre and variability together, and explain the limits of a range-based claim.
Section 6.2 · Statistical measures
Exam scope and assessment notes
The first four lessons are shared Foundation work inherited by Higher; the last four are additional Higher spread, quartile and cumulative-frequency reading work. Keep the actual repeated observations and frequencies, state units, and distinguish an average from spread. Grouped midpoint means and within-class graph readings are estimates. The chosen discrete quartile rule is median of each half, excluding the overall middle observation when the count is odd; it is not claimed as a universal Pearson algorithm. Draw the horizontal and vertical guides yourself, use each graph’s own total, and retain a full plateau interval when an inverse is not unique. Fixed numerical agreement does not mark reasoning or award marks or mastery. Specialist and formal-assessment approval remain pending. This is the Issue 2 linear 4MA1 scope: November 2026/June 2027 entries and regional-centre eligibility differ from modular awards. Literal second-edition Book2 Handling Data4/5/6 and Hub tags guide navigation only; no Book1 6.2 tag or verified chapter/Hub body is inferred. Handling Data6 map208–222 conflicts with proof chapter207/summary221/next222; Handling Data4 map39–60 extends beyond summary59 before next61. Graphical representation6.1 and probability6.3 remain separate.
Before you begin
- Calculate means, medians and ranges.
- Compare quantities in the same units.
- Interpret shifts and positive multiplication.
Work on paper. Try each question before revealing help, and explain why your method works.
Quick readiness check
Which statement describes spread rather than a typical value?
What you will learn
- Distinguish measures of spread from averages.
- Compare numerical centre and range in like-for-like contexts.
- Explain the effects of extreme values, common shifts and positive scaling without overstating what a summary proves.
Two machines fill units with deviations A = [8, 9, 10, 11, 12] and B = [2, 6, 10, 14, 18] in the same units. Compare their means and ranges and describe which is more consistent.
Enter your own actual observations, positions, products, operations and guide endpoints. No count, total, role, result, quartile, graph height, unit or precision is filled for you. Recording is an attempt, not a correctness result.
Original supplied observations and tables
Only literal observations, frequencies, intervals and supplied cumulative guides are shown initially. The display does not sort observations, compute a total, average, quartile, rank or recovered value, or prefill a learner operation.
Blue denotes literal supplied guides, amber actual entered work, purple dirty retained work, and green a separately gated reference. Wrong and partial values are never repaired. The drawing bound is±1,000,000 coordinates, with at most512 scalar entries and128 cumulative points; these are tool bounds, not mathematical laws. Native clipping does not clamp values. No completed target is silently substituted.
The selected median-of-halves convention excludes the overall middle observation for odd lists. Grouped means and guide read-offs remain estimates. A zero-frequency plateau can give an interval rather than a unique reading. Fixed guide comparisons use the separate canonical700×500 policy with each source total; the tolerance is not a current-screen pixel or official marking tolerance. Text presence, operation consistency and numerical self-check do not approve reasoning, statistical claims, marks, mastery, teacher/specialist or formal assessment. No probability or new boxplot teaching credit is granted.
Order A; mark central positions, modes and extreme pair; retain total terms
Observation positions start at 1. Enter the repeated observations and each selected position yourself.
No actual work is recorded for this action.
Choose an explicit mode statement.
This is symbolic or one-dimensional work. These controls do not turn observation positions, row indices, extremes or interval boundaries into Cartesian points.
My quantities, conclusions and explanation
For a numerical list, use comma-separated values with no brackets or one balanced enclosing pair, or type none when you mean no mode. State the actual unit and exact, estimate or one-decimal-place precision yourself.
Retain the stated data, units, signs and precision. Numerical agreement is a self-check only.
Retain the stated data, units, signs and precision. Numerical agreement is a self-check only.
Retain the stated data, units, signs and precision. Numerical agreement is a self-check only.
Retain the stated data, units, signs and precision. Numerical agreement is a self-check only.
Retain the stated data, units, signs and precision. Numerical agreement is a self-check only.
Retain the stated data, units, signs and precision. Numerical agreement is a self-check only.
A written attempt needs actual reasoning; text presence does not approve the argument.
A written attempt needs actual reasoning; text presence does not approve the argument.
These are original fixed cases for self-checking, not automatic marking, mastery or teacher/specialist/formal-assessment approval. The stated median-of-halves convention is a chosen convention. Grouped midpoint means and within-class CF readings remain estimates; a plateau can give a full interval. A physical instrument and calibrated graph are separate from the screen. No probability credit follows.
Level and variability answer different questions
A typical value describes the level of a data set, while a measure of spread describes how widely its observations are distributed. Two groups can have the same mean or median but different extreme-to-extreme spans. Calculate and discuss both features when a comparison asks about consistency or variability.
The range is maximum minus minimum, so it uses only two observations. The interquartile range, studied next, uses the lower and upper quartiles to describe the central part of a distribution. Both are measured in the original data unit. A measure of spread is not a probability, an average, or a count of how many values differ.
Use comparisons that the context supports
A smaller range can support a statement that the observed values occupy a narrower overall span. In a repeated-process context that may be described as greater consistency by the range criterion. It does not automatically make the group better: a target level or a purpose is needed to decide whether a higher or lower typical value is desirable.
Because range uses only the extremes, changing one extreme value can change it substantially while leaving most of the data unchanged. The median can remain unchanged in a particular example even when the mean moves. That is a feature of the actual ordered positions in that example, not a rule that every unusual value has no effect on every median.
Preserve the limits of the evidence
Adding the same amount to every observation changes both extreme values by that amount, so their difference stays unchanged. Multiplying every value by a positive scale factor multiplies the extreme difference by that factor. Keep the units and the original comparison purpose visible when interpreting either operation.
A smaller range alone cannot prove that every observation is closer to a chosen centre, that the distributions have the same shape, or that an unobserved future reading will lie inside the old extremes. Use actual values to test such stronger claims. Numerical summaries support an explanation; they do not replace the information needed for it.
Sources for this lesson
- Pearson Mathematics A linear specification · Issue 2 · Foundation printed24/physical30; Higher printed39/physical45; inheritance printed27/physical33
- Pearson 2026/2027 entry manual · centre regions physical3; Mathematics A physical25
- Pearson second-edition modular book/Hub map · literal navigation only · physical9,10,11
Pause and explain
If the same constant is added to every value, what happens to the range?
Worked example
Two machines fill units with deviations A = [8, 9, 10, 11, 12] and B = [2, 6, 10, 14, 18] in the same units. Compare their means and ranges and describe which is more consistent.
Show the worked solution
- Both totals are 50 across five readings, so both means are 10; both medians are also 10.
- A has range 12 − 8 = 4 while B has range 18 − 2 = 16.
- A is less spread out by the range criterion. Equal means do not mean equal variability, and the task does not state a target that would make a larger average better.
Answer A: mean = 10, median = 10, range = 4. B: mean = 10, median = 10, range = 16, in the stated units. A has the narrower observed span and is more consistent by the range criterion.
Try a topic test
Choose Easy, Normal or Difficult. Save your answers and open worked review after finishing.
Test this topic →From guided practice to a new situation
Write your answer and reasoning first. Use a hint only if you are stuck. The model solution is for self-checking; your written work is not automatically marked.
Waiting times A = [4, 5, 6, 7, 8] and B = [4, 5, 6, 7, 28] minutes differ in one extreme reading. Compare their means, medians and ranges, and explain what the extreme changes.
Give me a hint
Calculate both centres and both ranges before describing the effect of the changed reading.
Compare my reasoning
- A has total 30, mean 6 and median 6 minutes. B has total 50, mean 10 but still median 6 minutes.
- The ranges are 8 − 4 = 4 and 28 − 4 = 24 minutes.
- The single high value changes mean and range strongly while leaving this median unchanged. This illustrates sensitivity, not a guarantee that every possible outlier leaves a median unchanged.
A: mean = 6, median = 6, range = 4. B: mean = 10, median = 6, range = 24, in the stated units. The single high reading raises B’s mean and range, while this median stays 6 minutes.
Look for these in your work
- Calculate the stated centre and spread.
- Explain their different roles in context.
- Avoid a claim stronger than the summaries justify.
Data A = [1, 4, 5, 6, 9] and B = [2, 2, 5, 8, 8] have the same units. Calculate means, medians and ranges. A claim says B having the smaller range proves every B value is closer to 5 than every A value. Decide whether that claim follows.
Give me a hint
Test the stronger every-value claim using actual distances, not just the ranges.
Compare my reasoning
- Each total is 25, giving mean 5; each middle value is 5.
- Ranges are 9 − 1 = 8 and 8 − 2 = 6, so B has the smaller overall extreme-to-extreme span.
- The stronger claim is false: A contains 4 and 6 only 1 unit from 5, whereas B contains 2 and 8 which are 3 units away. A summary range does not describe every individual distance.
A: mean = 5, median = 5, range = 8. B: mean = 5, median = 5, range = 6, in the stated units. B’s smaller range does not prove the every-value claim: B contains values 3 units from 5, while A contains values only 1 unit from 5.
Look for these in your work
- Calculate the stated centre and spread.
- Explain their different roles in context.
- Avoid a claim stronger than the summaries justify.
Two sets of lengths are A = [16, 18, 20, 22, 24] and B = [18, 19, 20, 21, 22] cm. Compare mean and range. Then find what happens to B’s mean and range if 100 cm is added to every length, and separately if every original B length is doubled.
Give me a hint
Track the two extreme values after each stated shift or positive scaling.
Compare my reasoning
- Both original means and medians are 20 cm. A has range 8 cm and B has range 4 cm.
- Adding 100 cm to every B value gives mean 120 cm but range (22 + 100) − (18 + 100) = 4 cm.
- Doubling the original B values gives mean 40 cm and range 8 cm. A translation changes level, whereas positive scaling changes both level and spread; comparisons still need compatible units and purposes.
A: mean = 20, median = 20, range = 8. B: mean = 20, median = 20, range = 4, in the stated units. For B + 100, mean = 120 cm and range = 4 cm. For 2B, mean = 40 cm and range = 8 cm.
Look for these in your work
- Calculate the stated centre and spread.
- Explain their different roles in context.
- Avoid a claim stronger than the summaries justify.
Common mistakes
- Treating a smaller spread as automatically a better outcome.
- Assuming equal means imply equal variability.
- Using a single range comparison to make a claim about every individual observation.
What can you explain now?
Does a smaller range prove that every value in one set is closer to its mean than every value in another set?
Compare with the explanation
No. Range records only the two extremes; the internal observations may have many different arrangements.
A stronger statement about individual distances needs the actual values or further distributional information.
After trying it yourself, choose your next review. This is your self-assessment.
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Make it stick
Describe how equal centres can coexist with different spreads, and identify one valid and one unjustified conclusion from a range comparison.
If you needed the solution, close it and solve the problem again from a blank page. A correct answer is stronger when you can explain the reason for each step.