← Statistics for Data Science learning pathStatistics for Data Science · Module 6 of 10
Hypothesis testing
Test claims with data using p-values.
📖 Key ideas
- Null hypothesis (H0): no effect or no difference.
- Alternative hypothesis (H1): there is an effect.
- p-value: probability of data at least this extreme if H0 is true.
- If p < α (often 0.05), reject H0.
- Type I error: false positive; Type II: false negative.
💡 Real-life example: Hypothesis testing is a court trial: assume innocent (H0) until evidence is strong enough to convict.
Null hypothesisAssumes no effect
p-valueEvidence measure against H0
Significance level αThreshold like 0.05
Type I errorRejecting a true null