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Both. Mathematical performance reflects genetic differences, learning environments, instruction and practice. Practice can build knowledge and problem-solving skill, while inherited differences help explain why people vary and why some skills remain more stable over time. These influences interact; neither “talent” nor “effort” sets a proven, permanent ceiling for one person.
The most useful conclusion is practical: you can become better at mathematics through well-designed learning, but improvement is not the same as proving that everyone starts with identical aptitudes or that one study can calculate your fixed potential.
What “math ability” actually includes
Math ability is not one trait. A school grade may combine arithmetic fluency, language comprehension, working memory, spatial reasoning, conceptual understanding, persistence and familiarity with the tested material. Someone can be quick with calculations but inexperienced with proofs, or understand algebraic ideas while making avoidable arithmetic errors.
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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Research therefore measures particular tasks, ages and populations. A finding about performance on an advanced combinatorics exam cannot be treated as a measurement of every form of mathematical ability. Likewise, a heritability estimate describes differences among people in a defined sample; it does not assign a genetic percentage to an individual student.
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What twin studies say about talent and environment
The UK longitudinal twin sample
A 2007 Society for Research in Child Development monograph analyzed a large, representative UK twin sample assessed at ages 7, 9 and 10. For English, mathematics and science, the authors reported high heritability and modest shared-environment influence at all three ages.
The longitudinal pattern is especially important. Genetic influences were associated mainly with stability from one age to the next, while environmental influences contributed to age-to-age change. This does not mean genes “caused” a particular child’s improvement or that teaching is weak. It means that, in this population and age range, differences that persisted over time were more strongly associated with genetic differences, whereas changes were more associated with environmental differences.
The Netherlands self-report study
A separate 2009 Behavior Genetics study used 1,685 twin pairs aged 12–24 from the Netherlands Twin Register. Participants rated their own competence in nine domains, including mathematics, and the researchers distinguished ordinary-range aptitude from rare, self-reported exceptional talent.
Across all nine domains, the reported heritability ranges were 0.32–0.71 for aptitude and 0.50–0.92 for talent. Those ranges are not mathematics-only results. The paper also reported a mathematics exceptional-talent estimate of 0.87, but that figure came from self-ratings and a rare category, so it should not be read as a population-wide measure of mathematical ability or as a forecast for any particular person.
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| Study | Participants and measure | Reported result | How to interpret it |
|---|---|---|---|
| 2007 Society for Research in Child Development monograph | Representative UK twin sample assessed at ages 7, 9 and 10; mathematics, English and science performance | High heritability and modest shared-environment influence at each age; genetic influences were linked more strongly to stability, environmental influences to change | Population-level variance in a specific setting and age range, not a fixed genetic share of one child’s ability |
| van den Berg and colleagues, Behavior Genetics (2009) | 1,685 Dutch twin pairs aged 12–24; self-rated competence across nine domains, including mathematics | Heritability ranges of 0.32–0.71 for aptitude and 0.50–0.92 for talent across domains; mathematics talent estimate 0.87 | The ranges are cross-domain, and the mathematics estimate is self-reported with a rare exceptional category |
Why heritability does not mean “my ability is 70% genetic”
Heritability is a statistic about variation within a population under particular conditions. If researchers say a trait has a heritability of 0.70 in one sample, they are describing how much of the observed differences among people in that sample are statistically associated with genetic differences. They are not saying that 70% of each person’s ability comes from genes.
Change the population, age, educational system, economic conditions or test, and the estimate can change. Heritability also says nothing about whether a trait can be changed. Height is highly heritable, yet nutrition and illness affect growth. In the same way, a mathematical skill can show genetic variation and still respond to teaching, practice, opportunity and feedback.
Twin studies are useful because identical and fraternal twins differ, on average, in the proportion of their genes they share. But the method relies on assumptions about environments and measurement. It cannot identify a single “math gene,” and it cannot reveal an individual’s maximum attainable score.
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Genes and environments can reinforce each other
Nature and nurture are not competing explanations that operate in sealed compartments. A child who finds number puzzles rewarding may choose them more often, receive more encouragement, seek harder books and eventually attract mentors. Those experiences can amplify an initial difference. Another child may have equal potential for growth but fewer opportunities, less relevant instruction or anxiety that reduces practice.
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The authors of the 2009 twin study explicitly reject the idea that heritability makes environment irrelevant. They describe a gene–environment-correlation view in which genetic differences, environmental exposure, practice and the way environments develop around ability all interact. Their conclusion states: “This gene-environment correlation view on the variability observed in aptitude and talent thus unites the views that practice is indispensible and that heritability at the same time is clearly of importance as well and merits further research.” The original spelling “indispensible” is retained in that quotation.
What effective practice looks like
Practice is not simply completing a large number of similar worksheets. A 2017 review of mathematics skill development describes deliberate practice as intentional work directed at improvement. It includes solving problems, thinking about why a method worked or failed, examining errors and changing future training in response to what was learned.
Use problems that are difficult but reachable
If every problem is automatic, it mainly tests recall. If every problem is far beyond the learner’s current knowledge, the learner may guess or copy without building a usable method. Choose tasks that require a decision, expose a misconception or connect ideas that are already partly understood.
Make feedback diagnostic
A score tells you whether an answer was accepted; useful feedback explains the step where reasoning diverged. After an error, identify whether the cause was a definition, a strategic choice, an algebraic manipulation, a calculation or a communication gap. Then solve a related problem without looking at the previous solution.
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Reflect and adjust the next session
Keep a short error log: the problem type, the first incorrect step, the corrected idea and a similar problem to revisit later. This turns practice into a sequence of decisions rather than a record of pages completed. The review does not establish one ideal daily duration or a guaranteed program, so avoid promises such as “a fixed number of hours will make anyone good at math.”
What a randomized course trial adds
A February 2026 IZA Discussion Paper by Agarwal and Gaule offers unusually direct evidence that instruction can improve learning in a defined context. The randomized trial included 620 highly gifted students from 44 countries, nominated by national Olympiad organizations. Students were assigned either to an 18-week advanced combinatorics course by Art of Problem Solving or to independent study using equivalent materials.
Assignment to the course increased final-exam performance by 0.16 standard deviations. The estimated gain among students who were fully engaged was 0.66 standard deviations. However, roughly half of those assigned to the course participated minimally, and assignment itself did not reliably create engagement.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteThis result supports a precise claim: structured advanced instruction can improve learning for highly selected students studying advanced combinatorics. It does not establish that the same effect applies to all ages, ordinary classrooms, every branch of mathematics or every learner. It also shows why access alone is not enough; sustained participation matters.
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How to turn the evidence into a learning plan
- Identify the exact bottleneck. Separate missing prerequisite knowledge from weak strategy, slow fluency, reading difficulty, test anxiety or careless execution. A broad label such as “bad at math” hides the problem you can actually address.
- Choose a level just beyond current independence. Start with examples you can partly explain, then move to unfamiliar variations. For advanced learners, that may mean proof or combinatorics problems; for beginners, it may mean fractions, equations or interpreting graphs.
- Attempt before consulting a solution. Write what is known, what must be shown and which definitions or theorems might apply. A serious attempt gives feedback about your reasoning even when the final answer is wrong.
- Study the solution actively. Do not copy line by line. Close the solution and reconstruct its key decisions, then solve a nearby problem from scratch.
- Space revisits. Return to previously missed ideas after an interval and test whether you can use them in a new context. The goal is flexible retrieval, not recognition of a familiar page.
- Get outside feedback when progress stalls. A teacher, tutor, study group or well-designed course can expose a misconception that self-study keeps repeating. Match the support to the learner’s level rather than choosing the most advanced label available.
- Protect engagement. Set a manageable session goal, vary problem types and track explanations, not just scores. The 2026 trial shows that enrolling in advanced training does not guarantee participation.
How to interpret your own progress
Improvement is evidence that learning occurred, not proof that you had no initial aptitude. Conversely, a plateau does not prove a fixed limit. It may indicate that prerequisites are missing, problems are badly matched, feedback is too vague, practice is too repetitive, or anxiety and fatigue are interfering.
Compare performance on similar tasks over time, but also examine the quality of your reasoning. A learner who takes longer yet can explain a method, detect an error and adapt it to a new problem may be developing more durable ability than a learner who produces quick answers by memorized pattern.
Common claims that the evidence does not support
- “Genes determine a fixed fraction of my math ability.” Heritability is a population statistic, not an individual percentage.
- “High heritability means teaching cannot help.” The UK longitudinal findings associated environmental influence with age-to-age change, and the randomized course trial found an instructional effect in its selected sample.
- “Practice alone explains exceptional expertise.” The twin evidence supports genetic contributions to individual differences as well as the importance of practice and environment.
- “The 0.87 figure means mathematical talent is 87% genetic for everyone.” It was a self-reported estimate for exceptional mathematical talent in one Dutch study, whose broader ranges covered nine domains and whose exceptional group was rare.
- “Any advanced course will produce the same gain.” The 2026 result concerns highly gifted, Olympiad-nominated students, an 18-week combinatorics course and engagement that varied widely.
So, talent or practice?
The strongest answer is an interaction. Genetic differences help explain why people differ in measured performance and why some trajectories are more stable. Environments, instruction and deliberate practice help explain change and can develop mathematical knowledge and problem-solving skill. A learner’s next step is therefore not to estimate an inherited ceiling, but to find the specific skill to improve, choose appropriately challenging work, obtain informative feedback and sustain engagement long enough for learning to accumulate.
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