If p-value & gt ; α , we ‗ fail to reject H0 ‘, The data did not provide significant evidence to reject H0 . Please note that we never ‗ accept H0 ‘. The problem of ‗ accepting ‘ is that this wording makes it seem that we are convinced that the null hypothesis is true . ( Business Statistics ) Chapter 8 January 4 , 2017 10 / 48 Introduction to Hypothesis Testing The p-value and Significance Level Hypothesis testing is meant to reject the null hypothesis when the evidence is convincingly against it . If we collect only a small sample , we may not see convincing evidence for the alternative hypothesis because the sampling error is so large . For instance , if we observed three births and observed two boys , we would not be willing to accept the hypothesis ‗ 2 / 3 of all births are boys ‘ even though the data doesn ‘ t provide evidence that it is false . ( Business Statistics ) Chapter 8 January 4 , 2017 11 / 48 Hypothesis Testing for p Hypothesis Testing for p The pharmaceutical company is claiming a hypothesis , or wanting to show , that fewer than 20 % of the patients who use a particular medication experience side effects . In a clinical trial with 400 patients , they find 68 patients experienced side effects . Initially we would assume that 20 % or more of patients who use a particular medication experience side effects . ( Business Statistics ) Chapter 8 January 4 , 2017 12 / 48 Hypothesis Testing for p Hypothesis Testing for p This can be written as : H0 : 20 % or more of the patients who use a particular medication experience side effects . H1 : Fewer than 20 % of the patients who use a particular medication experience side effects . We can also summarize this as : H0 : p ≥ 0.20 H1 : p & lt ; 0.20 where p is the true proportion of patients who use a particular medication that experience side effects . ( Business Statistics ) Chapter 8 January 4 , 2017 13 / 48 Hypothesis Testing for p Hypothesis Testing for p Once we have the hypotheses stated , we then need to calculate the