Hypothesis testing formalises the steps of the decision-making process. Starting with an assumption about a population parameter of interest, a description of what values the sample statistic might take (based on this assumption) is produced: this describes what values the statistic is expected to take, just through sampling variation. This sampling distribution is often a normal distribution, or related to a normal distribution.
The sample statistic (the estimate) is then observed, and a test statistic, which often is a \(t\)-score, is computed to describe this sample statistic. Using a \(P\)-value, a decision is made about whether the sample evidence supports or contradicts the initial assumption, and hence a conclusion is made. Since \(t\)-scores are like \(z\)-scores, \(P\)-values can often be approximated using the 68–95–99.7 rule.