Descriptive vs inferential statistics
IB PsychologyΒ· Research methodologyΒ· 11 min read
1. Core concept
Statistics do two jobs. Descriptive statistics summarise the data in hand β a mean, a range, a correlation coefficient β so we can see the pattern in one sample. Inferential statistics then ask whether that pattern is likely to hold beyond the sample or is just chance, reporting statistical significance and often an effect size (how big the effect is, not just whether it exists). Interpreting a study well means reading both: what the numbers say, and how confident we can be that they generalise.
2. Key studies
IB Psychology answers must be supported by named studies. Learn these to cite as evidence:
Giletta et al. (2021)
A meta-analysis found a small but statistically significant and robust effect of peer influence on youth behaviour across countries and ages. β Shows the two together β a descriptive effect size (small) plus an inferential result (statistically significant) β and why both are needed to interpret a finding.
3. Evaluation (AO3)
Strength β separates signal from noise β Inferential tests guard against reading a random blip as a real effect, and effect size stops us over-selling a real-but-tiny one.
Limitation β significance is widely misread β A significant result is not automatically large or important, and a non-significant one does not prove there is no effect β both are routinely over-interpreted.
4. Scope
5. Common Pitfalls
Wrong move:
'Statistically significant' means the effect is large or important.
Why:
A frequently penalised misconception in IB Psychology exams.
Correct move:
Significance only means the result is unlikely to be chance; a tiny effect can be significant in a large sample. Report the effect size alongside significance to judge importance.
Going deeper
- Descriptive statisticsthe descriptive-statistics detail is RM_5.
- Inferential statisticsthe inferential-tests detail is RM_6.