# Descriptive vs inferential statistics

> IB Psychology · IB Psychology 2027 First Assessment
> Source: https://www.owlsprep.com/study/ib-psychology-rm-descriptive-vs-inferential-statistics/

Analyse and interpret descriptive and inferential statistics.

## Learning objectives

- Analyse and interpret descriptive and inferential statistics.

## Core concept

Statistics do two jobs. Descriptive statistics summarise the data in hand — a mean, a range, a correlation coefficient — 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.

## 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. — *Shows the two together — a descriptive effect size (small) plus an inferential result (statistically significant) — and why both are needed to interpret a finding.*

## Evaluation (AO3)

**Strength — separates signal from noise** — 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 — significance is widely misread** — A significant result is not automatically large or important, and a non-significant one does not prove there is no effect — both are routinely over-interpreted.

## Scope

> **note**
>
> The distinction and the interpretation skill; the descriptive measures are RM_5 and the inferential tests RM_6.

## Common pitfalls

- **Wrong:** 'Statistically significant' means the effect is large or important.
  - Why it fails: A frequently penalised misconception in IB Psychology exams.
  - Correct: 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.

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