| Feature | Quantitative Data | Qualitative Data |
|---|---|---|
| Nature | Statistical, numerical, objective | Descriptive, artistic, subjective |
| Strengths | Easy to compare across time/space | Captures lived experience and emotion |
| Weaknesses | Can hide outliers or human nuance | Difficult to verify or generalize |
| Examples | Census, house prices, GIS | Novels, paintings, social media |
Identify the 'Hidden' Narrative: In exam questions, always look for what the data doesn't show; for example, a high average income might hide significant pockets of poverty.
Compare Perspectives: Be prepared to contrast an 'insider' perspective (e.g., a local's song) with an 'outsider' perspective (e.g., a government report).
Check the Scale: Always verify if the data is presented at a local, regional, or national scale, as patterns can change drastically depending on the level of detail.
Verify Provenance: Always mention the reliability of the source based on its author and date to gain marks for critical evaluation.
The 'Data is Truth' Fallacy: Students often assume quantitative data is 100% accurate, forgetting that statistics can be manipulated or contain errors in collection.
Ignoring the Context: Analyzing a photograph or painting without considering the historical or social context in which it was produced leads to shallow interpretations.
Over-generalization: Assuming that one qualitative source (like a single novel) represents the views of an entire population is a common analytical error.