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This lesson covers the fundamentals of hypothesis testing at A-Level. Hypothesis testing is a formal procedure for making decisions about a population parameter based on sample evidence. It is one of the most important topics in A-Level statistics and appears frequently in examinations.
A hypothesis test uses sample data to assess evidence against a claim about a population parameter.
| Term | Definition |
|---|---|
| Null hypothesis H0 | The default assumption; assumed true unless there is strong evidence against it |
| Alternative hypothesis H1 | The claim we are trying to find evidence for |
| Test statistic | A value calculated from the sample data used to decide the outcome |
| Significance level α | The probability of rejecting H0 when it is actually true (usually 5% or 1%) |
| Critical value | The boundary value that determines the rejection region |
| Critical region | The set of values of the test statistic that would lead to rejection of H0 |
| p-value | The probability of obtaining the observed result (or more extreme) assuming H0 is true |
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