Hypothesis Testing

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.

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The Hypothesis Testing Framework

A hypothesis test uses sample data to assess evidence against a claim about a population parameter.

Term	Definition
Null hypothesis $H_0$H0​	The default assumption; assumed true unless there is strong evidence against it
Alternative hypothesis $H_1$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 $\alpha$α	The probability of rejecting $H_0$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 $H_0$H0​
p-value	The probability of obtaining the observed result (or more extreme) assuming $H_0$H0​ is true

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