Lesson 11
Hypothesis testing intuition
Big question
How do we decide whether a data pattern is surprising?
Lesson progress
Complete checkpoints as you learn
Learning objectives
- Explain hypothesis testing intuition in plain language.
- Use null hypothesis correctly in an interpretation.
- Connect the lesson idea to a formula, graph, Python result, or real example.
Simple explanation
Hypothesis testing starts with a baseline claim, then asks whether the data would be unusual if that claim were true. The goal is not certainty; it is disciplined evidence.
Key terms
- Null hypothesis
- The baseline claim we test against, often no effect or no difference.
- Alternative hypothesis
- The competing claim suggested by the question.
- P-value
- A measure of how surprising the data are under the null hypothesis.
- Statistical significance
- A judgment that a result is unlikely under the null at a chosen cutoff.
Common null
Example
If we test whether education is related to wages, the null might say the education slope is zero.
Checkpoint activity
Pause and explain this lesson's main idea in your own words before moving forward.
Try it yourself
Write one plain-English sentence explaining the main idea from this lesson.
Common mistakes
Check these before you move on.
A regression coefficient describes a pattern unless the assumptions or research design support a causal interpretation.
Quick quiz
What does the null hypothesis often represent?
Key takeaway
Hypothesis testing helps separate strong evidence from patterns that could easily arise by chance.