In one line: Data only means something once you can read it, summarise it, and judge how far it can be trusted.
Data analysis and interpretation is studied by all psychology students (assessed HL-only on Paper 3). It covers reading graphs, summarising data with statistics, judging significance, and analysing qualitative data through themes.
This links directly to the concept of measurement: analysis turns raw numbers and words into meaning. On Paper 3, you interpret sources and data, so these skills are essential.
Memory hook: Read it, summarise it, judge it. Data analysis makes evidence meaningful.
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Key idea: Quantitative data is read through graphs and descriptive stats; qualitative data through themes.
The analysis toolkit
Graphs
Bar charts compare groups; box-and-whisker shows spread and median; scatter plots show a relationship between two variables.
Descriptive stats
Central tendency (mean, median, mode) and spread (range, standard deviation) summarise a data set.
Significance
A significant result is unlikely to be due to chance alone — but 'significant' isn't the same as large or important.
Thematic analysis
For qualitative data (interviews), identify recurring themes in what people say to capture meaning.
Graphs · Descriptives · Significance · Themes
Take a study comparing two teaching methods. A bar chart compares average scores; the mean and standard deviation summarise each group; a significance test asks if the difference is beyond chance; and if interviews were done, thematic analysis captures why. Each tool turns data into measurement-based meaning.
Go further — higher-level insight: Significant ≠ big ≠ important. A statistically significant result just means it's unlikely to be chance; it may still be a tiny effect with little real-world value. Distinguishing significance from effect size is a high-level Paper-3 interpretation point.
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Key idea: Good interpretation asks what the data can — and cannot — show.
So interpreting data means reading graphs carefully (watching for misleading scales), reporting spread as well as averages, and matching conclusions to the research design — a correlation can't support a causal claim. On Paper 3 you also weigh credibility, bias and transferability of the sources.
Watch out: A mean without spread can mislead. Two groups can share a mean yet differ hugely in variation — always look at the standard deviation or the box-and-whisker.