IB DP Maths AI · Exam special · SL & HL · model wording for R1 and A1 marks · ~9 min read
IB Maths AI Answer Templates: Interpret, Justify, Conclude
Fill-in-the-blank model answers for the IB Maths AI questions where examiners say students lose reasoning marks: hypothesis tests, interpreting parameters, regression, comparing data, Voronoi, finance and model limitations.
Interpreting gradients & parametersRegression & reliabilityComparing distributionsχ² testst-testsHL tests & intervalsVoronoi justificationsFinance answers
RSby Riyaz Sir
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No tag = SL and HLHL ONLY HL students onlyFORMULA BOOKLET given in the examM1A1 how marks are awardedhighlight = comes up again and againHANDY worth memorisingTest yourself at the end
In 30 seconds
- AI marks reasoning in words. Every AI subject report in 2024–25 says students lose R1 and A1 marks by writing generic or incomplete statements: "reject H₀" with no context, "a is the gradient", "it's reliable because r is high".
- The fix is a template. Each one below has the parts examiners look for. Swap in the context of the question and keep the structure.
- The number of marks tells you how much to write. A 1-mark "interpret" needs one precise sentence; a 6-mark "justify" needs calculations and a conclusion.
By the end you can
- Interpret gradients, intercepts and model parameters in context.
- Write complete hypothesis-test answers that earn every mark.
- Judge the reliability of predictions and the limitations of models.
- Justify Voronoi and finance answers with the evidence examiners want.
1Interpreting models
Linear models
- Template (gradient): "For each extra [1 unit of x, with units], [y] increases (or decreases) by [gradient, with units]."
- Template (intercept): "When [x = 0, in context], [y] is [intercept, with units]." Add "(the fixed cost)" or similar if it has a meaning.
Worked example
A model for a city's population is P = 0.362t + 30.5 (millions), t years after 2000. Interpret 0.362 and 30.5.
- "The population increases by 0.362 million people per year." R1
- "In 2000 (t = 0) the population was 30.5 million." R1
Common mistakeWriting "0.362 is the rate of change" or "the population increases by 0.362". Examiners want the change in y per unit of x, with the units (here millions per year).
Exponential and sinusoidal models
- Exponential y = k × aˣ + c: "k + c is the value when x = 0; a is the growth factor each [unit], so it grows by (a − 1) × 100% each [unit]; c is the long-term value the model approaches."
- Logistic (HL): "L is the carrying capacity: the maximum [population] the model predicts in the long term."
- Sinusoidal y = a sin(b(x − c)) + d: "ǀaǀ is the amplitude (half the difference between the highest and lowest values); d is the principal axis, the mean level; the period is 360°/b: the time for one full cycle."
Worked example
The depth of water is D = 1.8 sin(30t)° + 3.2 metres, t hours after midnight. Interpret 3.2 and the period.
- "3.2 m is the mean depth (the principal axis)." R1
- period = 360/30 = 12, so "the tide repeats every 12 hours." A1
2Regression and correlation
- Describe r: "r = 0.912 shows a strong positive linear correlation between [x] and [y]." Use strong (ǀrǀ ≥ 0.75), moderate, weak or very weak, and always say linear.
- Reliability of a prediction: "The prediction is reliable/unreliable because x = [value] is inside/outside the range of the data (interpolation/extrapolation) and the correlation is strong/weak." You need both reasons for full marks when the question asks "is this reliable?".
- Correlation vs causation: "A strong correlation doesn't show that [x] causes [y]; both might depend on [a third factor]."
Common mistakeUsing r alone to justify reliability. The Nov 2024 and May 2025 reports both saw this. A strong r doesn't save an extrapolation.
Comparing two data sets
- Template: "[Group A] has a higher median ([value] vs [value]), so A typically [context, e.g. waited longer]. [Group A] has a larger IQR ([value] vs [value]), so A's [context] are more spread out / less consistent."
Common mistakeComparing numbers without saying what they mean, or comparing a median with a mean. Compare like with like: median with median, IQR with IQR, and say which data set each number comes from.
3Hypothesis tests
χ² test for independence
- H₀: "[Variable A] and [variable B] are independent." H₁: "[Variable A] and [variable B] are not independent."
- Evidence: "p-value = 0.0631 > 0.05 (the significance level)" or "χ²_calc = 5.52 < 5.99 (critical value)".
- Conclusion: "There is insufficient evidence at the 5% level to reject H₀, so [favourite subject is independent of year group]." Or: "Reject H₀: there is evidence at the 5% level that A and B are not independent."
Common mistakeWriting H₀ as "there is no relationship", "A does not influence B" or "there is no association". The May 2025 SL report calls these incorrect. Use independent.
χ² goodness of fit
- H₀: "The data fits the [uniform / binomial B(4, 0.5) / normal] model." H₁: "The data does not fit the model."
- Degrees of freedom: number of categories − 1 (HL: subtract one more for each parameter you estimated from the data).
t-test for two means
- H₀: μA = μB. H₁: μA > μB (one-tailed) or μA ≠ μB (two-tailed), where μ is the population mean [score, time…].
- Conclusion: "p = 0.00697 < 0.05, so reject H₀: there is evidence at the 5% level that the mean [score] of A is greater than that of B."
- Assumptions (if asked): "both samples are random and independent, and both populations are normally distributed."
Exam tipSay "insufficient evidence to reject H₀", never "accept H₀". A test can't prove the null hypothesis.
HL tests and intervalsHL ONLY
- Binomial test of a proportion: "X ~ B(20, 0.3) under H₀: p = 0.3, H₁: p > 0.3. P(X ≥ 10) = 0.0480 < 0.05, so reject H₀: there is evidence that more than 30% of seeds fail."
- Confidence interval: "46.1 < μ < 50.3." Interpret: "We are 95% confident that the population mean [quantity] lies between 46.1 and 50.3." If asked about a claimed value: "17 °C is (not) inside the interval, so it is (not) a plausible value for the mean."
- Type I error (in context): "Concluding that [more than 30% of seeds fail] when in fact [30% fail]." Type II: "Not concluding that [more than 30% fail] when in fact [they do]."
- Symbols: write hypotheses with μ, p or ρ (population parameters), not with x̄ or r.
4Voronoi diagrams
- Nearest site: "Point P lies in the cell of site B, so B is the nearest [fire station]."
- Why a different site might respond: "P is close to the edge between B and C, so the distances are almost equal; [traffic, road layout, availability] could make C quicker."
- Coverage: "The furthest point of the cell from D is the vertex (6, 5), at a distance of 2.83 km < 3 km, so the whole cell is covered." The explicit comparison is the R1 mark.
- Toxic waste dump / new site: "The point furthest from all sites is a vertex of the Voronoi diagram (or on the boundary of the region): [coordinates], which is [distance] from its nearest sites."
5Finance answers
- Template: list the TVM entries, then the answer with the right accuracy and a sentence: "N = 60, I% = 6, PV = 20 000, FV = 0, P/Y = C/Y = 12 ⇒ PMT = −386.66, so the monthly payment is $386.66."
- Interest vs rate: "Total interest = total paid − amount borrowed = $3199.36." A rate is a percentage; interest is money.
- Which option is better? Compare the same quantity (total cost, final value) and say which with the numbers: "Option B costs $1240 less in total, so B is better."
6Model limitations and "comment" questions
- Template: "The model predicts [impossible or unrealistic value] for [x], for example [a negative height / endless growth], so it is only valid for [domain]."
- Context first: reasons must be about the situation in the question. "Fiona can't run for an infinite time, so t is limited by the length of the race" scores; "the model is just a model" doesn't.
- "Explain why the answer is reasonable": compare with the information you were given ("8.9 m is more than the 8.5 m in the question, which is consistent").
7"Show that" in AI
- Start from the question's information and finish with the given answer as your final line. Never substitute the given value to check it.
- Show one more significant figure: to show r = 18.5, write r = 18.49… = 18.5.
- Re-enter: if you can't show it, use the given answer in the next part. Examiners note many students give up instead.
Linking questionThe tests and models here are taught in Chi-squared and t-tests, Statistics, correlation and regression and Modelling with functions. The GDC steps are in the GDC guide.
Test yourself — cover the answers first
Quick check
- Write H₀ for a χ² test on eye colour and hair colour.
- Which two reasons judge whether a prediction is reliable?
- How do you interpret b = 2.4 in C = 3 + 2.4d (dollars, d in km)?
- p = 0.21 at the 5% level. Write the decision.
- HL Write a Type I error for H₀: p = 0.3 vs H₁: p > 0.3 (seeds failing).
Show answers
- eye colour and hair colour are independent
- interpolation vs extrapolation, and the strength of correlation
- the cost increases by $2.40 per extra km
- insufficient evidence to reject H₀
- concluding more than 30% fail when really 30% fail
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