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

IA idea · Statistics & hypothesis testing

Do TV series really get worse over time?

AI SLAI HL Accessible Also in: Art & music

Research question

Across [several long-running series], is there a significant negative correlation between season number and mean episode rating, and are final seasons rated significantly differently from earlier ones?

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Why it makes a good exploration

Fans often say shows “go downhill”. IMDb's public datasets let you test that claim across many series rather than arguing about one.

The mathematics you'll need

Spearman's rank correlation
Linear regression
Two-sample t-test for final vs. other seasons
Weighted means (by number of votes)

Modelling the data, step by step

This idea usually needs a straight-line model. See it worked step by step, with a criterion tip at every step: Straight line y = mx + c.

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The statistics, step by step

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

Where the data comes from

Use IMDb's non-commercial datasets (title.episode and title.ratings); credit IMDb as required.

IMDb non-commercial datasets — Ratings, votes, runtimes and episodes for every title (non-commercial use, credit IMDb).

A possible outline

Choose a fair sample of series (e.g., all with 6+ seasons in a genre).
Compute season means.
Test for a trend within and across series.
Compare final seasons.
Reflect on who votes and review-bombing.

Pitfalls that cost marks

Choosing only famous flops (selection bias).
Ignoring the number of votes.
Treating episodes as independent.

Showing personal engagement

Include a show you love and one you gave up on.
Predict the result before analysing.
Compare genres.

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	Spearman's rank correlation; Linear regression
AI HL	Fits, but add an HL technique	Spearman's rank correlation; Linear regression

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: choosing only famous flops (selection bias); and ignoring the number of votes. Say how each one affects your answer.

Use of mathematics (E)

SL: spearman's rank correlation; linear regression; two-sample t-test for final vs. other seasons; weighted means (by number of votes), 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

Model how vote counts decline by season and test whether rating decline is linked to audience decline.

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?

Spearman's rank correlation, linear regression, two-sample t-test for final vs. other seasons and weighted means (by number of votes).

Where can I get data for this IA?

Use IMDb's non-commercial datasets (title.episode and title.ratings); credit IMDb as required. Checked sources and tools: IMDb non-commercial datasets.

Turn this idea into your IA

The planner opens with “Do TV series really get worse over time?” filled in: the question, the maths and the data sources.

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