Are popular songs getting faster or shorter? Statistics on music data

AI SLAI HLAA SL Accessible Also in: Statistics

Research question

Has the length or tempo of popular songs changed over the decades, is the change statistically significant, and is it the same across genres?

Adapt it: change the place, the data or the comparison until the question is yours.

Free: the A–E checklist an examiner uses, by email ↓

Why it makes a good exploration

A question about music you listen to, answered with regression and hypothesis tests on a large dataset, with plenty to reflect on about where the data comes from.

The mathematics you'll need

Descriptive statistics and box plots by decade
Linear regression of length or tempo against year
Correlation and its significance
t-test or chi-squared across groups
Discussing sampling and selection bias

Course labels show where a technique sits; using maths from outside your course is fine if you explain it clearly and say it is new to you.

The statistics, step by step

Worked with every number shown, with what examiners look for and the common mistakes: t-test: two-sample and paired · Pearson's correlation and regression · Descriptive statistics and box plots · Sampling and collecting data. Then run the same steps on your own data in Analyse my data, or start from the statistics workflow.

Where the data comes from

Find a song dataset with year, duration and tempo; Kaggle hosts several, so check the licence and the original source. Or build your own sample from charts and measure tempo by tapping.

Kaggle datasets — Community-uploaded datasets on almost any topic — check the licence and original source before you use one.
Desmos graphing calculator — Free graphing and regression (y₁ ~ ax₁ + b) — fit models to your data and show residuals.

Cite every source in a footnote where you use it and in your bibliography. Check the licence of any dataset you download.

A possible outline

Choose and clean the dataset; state its source.
Summarise by decade.
Fit and test trends.
Compare genres.
Reflect on how songs got into the dataset.

Pitfalls that cost marks

A dataset with unclear origin or licence.
Mistaking correlation with time for a cause.
Ignoring how tempo was measured.

Showing personal engagement

Use music you know and add your own measured songs.
Predict the trend first.
Ask a musician why it might have changed.

See Criterion C: personal engagement for what examiners look for.

Which course is it for?

Course	Fit	Maths to lean on
AA SL	Good fit	Descriptive statistics and box plots by decade; Linear regression of length or tempo against year
AA HL	Not a natural fit	The mathematics is mainly from the AI course; at AA HL the exploration would need an AA-level approach (calculus, proof or probability theory) to reach the top of Criterion E.
AI SL	Good fit	Descriptive statistics and box plots by decade; Linear regression of length or tempo against year
AI HL	Fits, but add an HL technique	Descriptive statistics and box plots by decade; Linear regression of length or tempo against year

Level: Accessible. A good first extended piece of maths, with room to go deeper. See how the IA differs between AA and AI, SL and HL.

How this idea reaches the top bands

Personal engagement (C)

Use an artwork, instrument or design you know or make yourself, and measure it yourself. Explain the aesthetic question as well as the mathematical one.

Reflection (D)

Test popular claims honestly (the golden ratio is often not where people say it is) and reflect on how measurement choices affect the result. For this idea, start with: a dataset with unclear origin or licence — say how it affects your answer.

Use of mathematics (E)

SL: Geometry, trigonometry, sequences or functions used to model or test a real piece of art or music, with measurements and error discussed.

HL: Complex numbers, parametric curves, transformations as matrices, or Fourier-style sums of sinusoids, used to explain the structure rigorously.

Criteria A and B (presentation and communication) work the same way for every idea: see the guides to Criterion A and Criterion B.

Taking it further

Model song length with a piecewise function around a change in how music was sold or streamed, if your data shows one.

Extending it for HL

Describe the construction with parametric or complex-number methods, or prove a property of the pattern or scale you studied.

See a complete IA, marked

Our annotated exemplar Is a hanging chain a parabola? Comparing catenary and quadratic models (AA SL) asks a different question, but shows how a complete art & music exploration is structured and marked, with an examiner's comment on every criterion. Free excerpts and the full marking table are on its page.

Before you start: the checklist an examiner uses

Every check for Criteria A–E in a 4-page PDF, the mistakes that cost the most marks and a self-assessment grid. We'll email it with a short IA tip every few days, timed to your deadline if you give it. Free — no account, no payment.

Get the free checklist

Turn this idea into your IA

Plan this IAOpens the planner with this question, the maths and data sources filled in. Criteria checklistCheck your plan or draft against the A–E descriptors and self-grade before you hand it in. AI feedback on a draftCriterion-by-criterion feedback with evidence from your own text. Coaching, not rewriting. Examiner reviewPete, an IB examiner, reads your draft and talks it through with you (€100).