Assumptions, Robustness & Pitfalls
Info sheet · Statistics for Psychology & Neuroscience
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What you’ll get from this sheet
By the end you should be able to:
- Recognise when a test is robust to a violated assumption.
- Avoid the common inferential pitfalls (p-hacking, HARKing, misread p-values).
Tests vary in how robust they are to broken assumptions; know which violations matter. And guard against the recurring traps — p-hacking, HARKing, over-reading a non-significant result, and confusing significance with importance.
[Main idea]
TODO — write the core explanation in transcript voice.
[Interactive demo]
TODO — native <input> + string-built SVG / OJS widget (see any built sheet for the pattern).
See it in code
# TODO# TODO
TipCheck your understanding
TODO — a short question.
TODO — the worked answer.
TODO — a common mistake and how to avoid it.
Where this shows up next
TODO — link to the relevant book chapter.