Do we know how long we spend on our phones? Estimates versus screen-time data

AI SLAA SLAI HL Accessible Also in: Statistics, Health & biology

Research question

How closely do students' estimates of their daily screen time match the figure their phone records, do people tend to under- or over-estimate, and does the error grow with the amount of use?

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

Self-reported data is the weak point of most surveys, and here you can measure that weakness directly by comparing each answer with a recorded value.

The mathematics you'll need

Paired data and differences
Mean and standard deviation of the error
Scatter plot, correlation and regression of estimate against recorded time
Paired t-test (AI)
Percentage error and whether it grows with use

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. Then run the same steps on your own data in Analyse my data, or start from the statistics workflow.

Where the data comes from

Anonymous form: ask for the estimate first, then the phone's weekly average. Consent, no names, and your school's approval.

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

Design the form so the estimate comes before the check.
Pilot it and fix confusing wording.
Collect anonymous paired data.
Analyse the differences and test for bias.
Reflect on who chose to take part.

Pitfalls that cost marks

Letting people check their phone before estimating.
Collecting identifiable data.
Ignoring that phones measure 'screen time' differently.

Showing personal engagement

Start with your own estimate and data.
Predict the direction of the error.
Compare age groups or year groups.

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	Paired data and differences; Mean and standard deviation of the error
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	Paired data and differences; Mean and standard deviation of the error
AI HL	Fits, but add an HL technique	Paired data and differences; Mean and standard deviation of the error

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)

Design every part yourself: the question, the sampling frame, the wording and the analysis plan. Piloting the questionnaire and changing it is engagement an examiner can see.

Reflection (D)

Reflect on bias (who answered, who didn't, how wording steered answers) and on what a significant result can and can't show about the whole population. For this idea, start with: letting people check their phone before estimating — say how it affects your answer.

Use of mathematics (E)

SL: A sampling method justified, a sample size worked out from the test you plan, the test's conditions checked, one calculation shown and the p-value interpreted in context.

HL: Confidence intervals, a justified choice between tests, a test of a model's fit, or a statistical estimate of how much bias could change the conclusion.

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 the estimate as a power function of the recorded time and test whether people compress large values.

Extending it for HL

Add confidence intervals for the proportions you report, and estimate how large a non-response bias would have to be to overturn your result.

See a complete IA, marked

Our annotated exemplar Do students who sleep less react more slowly? (AI SL) asks a different question, but shows how a complete surveys 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).