# Client conclusion — Computer Science gender balance

**To:** Client (via Dr Amara Chen)  
**From:** Insight Education Analytics trainee (worked example)  
**Subject:** A-level Computer Science entries by sex, England, 2024/25

## Conclusion

My hypothesis was that A-level Computer Science entries in England in 2024/25 are not
gender-balanced, and specifically that the female share is below 50%.

The data showed **3,487** female entries and **15,195**
male entries (Female + Male matches the published All students total of
**18,682**).

The female share was **18.7%**, which is
**-31.3** percentage points away from 50%.

The binomial test gave a p-value of **0.00e+00** (effectively zero at
conventional reporting precision).

This means the observed split is **not** consistent with the null hypothesis of a 50/50
gender split. The imbalance is both large in practical terms and statistically extreme
under a fair-coin null.

However, this analysis only shows the pattern in **entries**. It does not explain why the
imbalance exists.

## Data quality and limitations

- The data is official DfE education data (Academic year 2024/25 release).
- The analysis uses national England data only.
- The analysis uses **entries**, not unique students.
- The data uses binary categories: Female and Male.
- The analysis tests whether the split differs from 50/50; it does not identify causes.
- Subject choice may be affected by school availability, prior attainment, confidence,
  family expectations, peer culture, teacher encouragement and wider social stereotypes.
- The All students row was used to check that Female + Male entries matched the published total.

## Chart

`outputs/charts/module3_cs_gender_bars.png`

## Optional contrast — Psychology

Psychology in the same year had **53,185** female and
**18,963** male entries (**73.7%** female;
difference from 50% = **+23.7** pp; binomial p ≈
**0.00e+00**). That contrast is gender-skewed in the opposite direction —
useful context, still not a causal explanation.