# Inferential statistics

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

Inferential statistics: Analyse and interpret results of typical tests of difference between two groups or conditions (such as chi-square test, related and unrelated t-test, Mann-Whitney test, Wilcoxon test) and tests of relationship between two variables (such as correlation coefficients). Understand the notions of effect size and statistical significance.

## Learning objectives

- Inferential statistics: Analyse and interpret results of typical tests of difference between two groups or conditions (such as chi-square test, related and unrelated t-test, Mann-Whitney test, Wilcoxon test) and tests of relationship between two variables (such as correlation coefficients). Understand the notions of effect size and statistical significance.

## Core concept

Inferential statistics test whether a result is likely to be real rather than chance, so a conclusion can extend beyond the sample. Tests of difference compare two conditions or groups (for example chi-square, related and unrelated t-tests, Mann-Whitney, Wilcoxon); tests of relationship measure how two variables co-vary (correlation coefficients). Each yields a significance level, and researchers also report effect size to show how large the effect is. Significance says 'probably not chance'; effect size says 'this much' — a strong conclusion needs both.

## Key studies

IB Psychology answers must be supported by named studies. Learn these to cite as evidence:

**Kuhn et al. (2019)** — A randomised controlled trial found no effect of eight weeks of violent video-game play on aggression or related measures. — *A test of DIFFERENCE between conditions returning a non-significant result — a reminder that inferential testing can, and should be able to, come back null.*

**Rosenquist, Fowler & Christakis (2011)** — Depressive symptoms were significantly correlated between people up to three degrees of separation in a social network. — *A test of RELATIONSHIP (a significant correlation) — but a correlation, however significant, does not by itself establish cause.*

## Evaluation (AO3)

**Strength — disciplined inference** — Significance testing puts a probability on chance, and pairing it with effect size prevents both false alarms and over-claiming.

**Limitation — not causation, not importance** — A significant test of relationship shows co-variation, not cause; and significance depends on sample size, so it can flag trivial effects in very large samples.

## Scope

> **note**
>
> The inferential tests and what they can conclude; the descriptive input is RM_5, and the causal caution is developed in RM_15.

## Common pitfalls

- **Wrong:** A significant correlation coefficient shows that one variable causes the other.
  - Why it fails: A frequently penalised misconception in IB Psychology exams.
  - Correct: Tests of relationship quantify how two variables move together; causation needs an experiment or ruling out confounds. Significance is about chance, not about cause or size.

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