Updated 12 October 2026 · By Pete Bromfield, former Head of Maths with IB examining experience

IA idea · Statistics & hypothesis testing

Is crime seasonal? Testing monthly crime counts

AI SLAI HL Accessible Also in: Environment

Research question

Are recorded [one crime type] offences in [one police force area] spread evenly across the months of the year once month length is allowed for, and is the pattern the same for a second crime type?

Make it yours, then agree it with your teacher.

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Why it makes a good exploration

“Crime by day of the week” is a popular idea, but the UK's open street-level data is published by month, not by day — so check what your data can support before you plan. Testing seasonality with monthly counts is the honest, answerable version, and comparing two crime types gives the chi-squared test real meaning.

The mathematics you'll need

Chi-squared goodness-of-fit test with expected counts proportional to days in each month
Chi-squared test of independence (month × crime type)
Degrees of freedom and expected frequencies
p-values and significance levels

The statistics, step by step

Worked with every number shown, with what examiners look for and the common mistakes: Chi-squared test for independence · Chi-squared goodness of fit. Then run the same steps on your own data in Analyse my data, or start from the statistics workflow.

Where the data comes from

Download street-level crime CSVs from data.police.uk for at least two full years for one force area.

data.police.uk — Street-level crime and outcomes for England, Wales and Northern Ireland by month (OGL v3).

Real data for this idea

Primary source: data.police.uk: Custom download. Choose one force and at least two full years; count rows per month for one crime type. Licence: Open Government Licence v3.0.

Links checked October 2026; we link to the data and never copy it. More free data sets.

A possible outline

Choose the crime types and area and explain why.
Check the data's date resolution and reporting notes.
Test one crime type against a days-in-month expectation.
Test independence between month and crime type.
Reflect on recording practices, holidays, daylight and weather.

Pitfalls that cost marks

Assuming exact dates are available — the open data is monthly.
Using equal expected counts for 28- and 31-day months.
Treating recorded crime as all crime.

Showing personal engagement

Choose an area you know and explain local factors (tourism, universities, nightlife).
Predict which months will stand out and why, then test.
Relate your finding to daylight hours or temperature data.

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

Which course is it for?

Course	Fit	Maths to lean on
AA SL	Not a natural fit	The core technique sits in the AI course or at HL; an AA SL student could use it only as clearly explained new mathematics.
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	Chi-squared goodness-of-fit test with expected counts proportional to days in each month; Chi-squared test of independence (month × crime type)
AI HL	Fits, but add an HL technique	Chi-squared goodness-of-fit test with expected counts proportional to days in each month; Chi-squared test of independence (month × crime type)

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

Reflection (D)

For this idea, start with the weak points: assuming exact dates are available — the open data is monthly; and using equal expected counts for 28- and 31-day months. Say how each one affects your answer.

Use of mathematics (E)

SL: chi-squared goodness-of-fit test with expected counts proportional to days in each month; chi-squared test of independence (month × crime type); degrees of freedom and expected frequencies; p-values and significance levels, each explained in your own words and carried out correctly.

How statistics explorations reach the top bands in general: Statistics & hypothesis testing IA ideas. Guides: A · B · C · D · E · self-grade sheet.

Taking it further

Correlate monthly counts with monthly temperature from the Met Office and discuss confounding (holidays, daylight).

Extending it for HL

Add an HL technique the question genuinely needs: what an HL statistics exploration adds.

See a complete IA, marked

Should I lease or buy my first car? (AI SL) is a complete statistics exploration, marked on every criterion.

Frequently asked questions

Which IB Maths course is this IA idea for?

It works for AI SL and AI HL. We rate it Accessible: a good first extended piece of maths, with room to go deeper.

What mathematics does this IA use?

Chi-squared goodness-of-fit test with expected counts proportional to days in each month, chi-squared test of independence (month × crime type), degrees of freedom and expected frequencies and p-values and significance levels.

Where can I get data for this IA?

Download street-level crime CSVs from data.police.uk for at least two full years for one force area. Checked sources and tools: data.police.uk.

Turn this idea into your IA

The planner opens with “Is crime seasonal? Testing monthly crime counts” filled in: the question, the maths and the data sources.

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