Hypothesis testing
A hypothesis test evaluates how compatible observed data are with a specified null hypothesis.
Null and alternative
The null hypothesis \(H_0\) specifies a reference claim, while \(H_1\) describes an alternative. A test statistic measures a relevant discrepancy between data and \(H_0\).
p-value
The p-value is the probability, assuming \(H_0\) and the test model are true, of obtaining a test statistic at least as extreme as the observed one.
It is not the probability that the null hypothesis is true.
Errors
A Type I error rejects a true null hypothesis. A Type II error fails to reject a false null hypothesis. Power is the probability of rejecting \(H_0\) under a specified alternative.
Key idea. Statistical significance measures evidence relative to a testing model; it does not by itself measure biological importance.