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IB DP Psychology — Research Methodology | SL & HL | Paper 3
🎯 Learning Intentions By the end of this note, you will be able to:
- Define inferential statistics, probability, and statistical significance using IB-aligned terminology.
- Explain the difference between a null hypothesis and an alternative hypothesis and the role each plays in statistical testing.
- Describe what a p-value represents and how it is used to make decisions about statistical significance.
- Distinguish between Type I and Type II errors and explain the conditions under which each is more likely to occur.
- Explain the concept of effect size and why it provides information beyond statistical significance alone.
- Evaluate the use of inferential statistics in psychological research using the concept areas of Causality, Measurement, and Bias.
- Apply understanding of inferential statistics to interpret findings from a psychological study presented in a Paper 3 source.
- Discuss the limitations of relying solely on p-values when drawing conclusions from psychological research.
Introduction & Context
Imagine you run an experiment to test whether listening to music while studying improves memory performance. You test 30 students — half study in silence, half study with music — and the music group scores, on average, 4 points higher on a recall test. That sounds promising. But here is the question that every psychologist must ask before drawing any conclusion: could that 4-point difference simply be the result of chance? Perhaps the music group happened to include slightly better memorisers by accident. Perhaps the difference would disappear if you ran the study again with different participants. The raw numbers alone cannot answer this question.
This is precisely the problem that inferential statistics was designed to solve. Descriptive statistics — means, medians, standard deviations — tell us what happened in our sample. Inferential statistics go further: they allow us to make a reasoned judgement about whether what we observed in our sample reflects a genuine pattern in the broader population, or whether it is simply the kind of variation we would expect by chance. Understanding this distinction is fundamental to evaluating any piece of psychological research.
🔍 Analogy — The Coin Flip Suppose you flip a coin ten times and get seven heads. Does that mean the coin is biased? It could be — or it could just be a lucky streak with a fair coin. Inferential statistics is like asking: "If this coin were perfectly fair, how often would I get seven or more heads in ten flips just by chance?" If the answer is "very rarely," you have reason to suspect the coin is biased. If the answer is "fairly often," you cannot rule out chance. Psychological research works the same way: we ask how likely our results would be if there were actually no real effect, and we use that probability to decide whether our findings are meaningful.
Core Concepts
Before any statistical test can be run, a researcher must be clear about what claim they are testing. In everyday life, we often frame questions as "does X cause Y?" or "is there a difference between these two groups?" But statistical testing requires a more precise framing — one that can be formally accepted or rejected based on the data. This is where hypotheses come in.
📖 Key Definition — Null Hypothesis (H₀) The null hypothesis is a statement that there is no significant difference, relationship, or effect between the variables being studied. It is the default position that the researcher attempts to disprove. For example: "There is no significant difference in recall scores between participants who study with music and those who study in silence."
📖 Key Definition — Alternative Hypothesis (H₁) The alternative hypothesis is a statement that there is a significant difference, relationship, or effect between the variables. It is what the researcher predicts will be supported if the null hypothesis is rejected. For example: "Participants who study with music will show significantly higher