IB Diploma Psychology HL · HL Extension: Data Analysis and Interpretation (first assessment 2027)
Mini-Lesson
HL extension: Data analysis and interpretation
At HL, you learn to analyse and interpret both quantitative and qualitative data from psychological research — and this is directly assessed. This lesson covers descriptive and inferential ideas, qualitative analysis, and how to draw sound conclusions.
Work through each screen, answer the questions to unlock the next one, and collect ⭐ stars. Press Start when you're ready.
HL extension · how it works
Why data analysis matters
The HL extension adds 45 assessed hours on handling data, linked to the class practicals and the IA research proposal.
You interpret data using the key concepts — especially measurement and bias.
Good analysis distinguishes what the data show from what a researcher might claim.
Key idea: statistics summarise and test; they never 'prove' — conclusions are always probabilistic and bounded by the design.
Quantitative · descriptive statistics
Describing quantitative data
Measures of central tendency: the mean (average), median (middle value) and mode (most frequent).
Measures of dispersion: the range and the standard deviation (spread around the mean).
Data are displayed with bar charts, histograms and scatterplots chosen to fit the data type.
Tip: the median is more robust than the mean when data are skewed or contain outliers.
Quick check
Which is right?
?Which is a measure of central tendency?
Quantitative · inferential ideas
Inference, significance and correlation
Statistical significance: a result is 'significant' when it is unlikely to be due to chance (commonly p < 0.05).
A correlation coefficient (e.g. r) shows the strength and direction of a relationship, from −1 to +1.
Significance is not the same as importance: a tiny effect can be significant in a large sample (consider effect size).
Caution: a significant correlation still does not establish causation.
Quick check
Which is right?
?In psychology, saying a result is 'statistically significant' (p < 0.05) means it is:
Qualitative · analysis
Analysing qualitative data
Thematic analysis identifies patterns (themes) by coding interview or observation transcripts.
Rigour is judged by credibility, transferability, dependability and confirmability (trustworthiness).
Reflexivity — reflecting on the researcher's own influence — and triangulation strengthen qualitative findings.
Contrast: qualitative analysis seeks meaning and depth, not statistical generalisation.
Quick check
Which is right?
?Thematic analysis is a method for:
Interpreting and concluding
Drawing sound conclusions
Match the claim to the design: experiments can support causal claims; correlational and qualitative work cannot.
State limitations (sampling, bias, generalisability) alongside conclusions.
Distinguish statistical significance from effect size and real-world importance.
Concept — responsibility: interpreting data honestly, without over-claiming, is an ethical duty.
Quick check
Which is right?
?A correlational study finds r = 0.6 between two variables. The most accurate conclusion is that they:
Sort it
Sort each term to its type
Tap an item, then tap the group it belongs to.
📊 Descriptive statistics
🧮 Inferential ideas
🗣️ Qualitative analysis
Concepts & research
Thinking critically about data
Measurement: the quality of conclusions depends on how well variables were operationalised and measured.
Bias: researchers can unintentionally bias analysis and interpretation — pre-registration and reflexivity help.
Perspective: the same data can support different interpretations, so transparency matters.
Report what the data show, the uncertainty around it, and the limits of the design.
Quick check
Which is right?
?Which pairing is correct?
Match it
Match each item to its idea
Tap on the left, then its match on the right.
Term
Meaning
Quick check
Which is right?
?Why is 'statistically significant' not the same as 'important'?