The Null Hypothesis and Statistical Power

When epidemiologists compare two human populations, one defined as being at risk and the other defined as the control, they begin by hypothesizing that there is no difference in disease frequency between the two populations. They then collect data to decide whether their hypothesis is correct or incorrect. The hypothesis of no difference between two populations is called the null hypothesis. The null hypothesis is accepted if it is decided that there is no difference between the two populations, and it is rejected if it is decided that there is a difference. There is a finite probability of committing an error and rejecting the null hypothesis when it should be accepted and of accepting the nnll hypothesis when it should be rejected. The decision to accept or reject the null hypothesis is associated with a specified level of statistical confidence in the data. For example, if the null hypothesis is rejected at the 0.95 confidence level, there is a 95% chance that the decision is correct (i.e., that there really is a difference between the study and control populations) and a 5% chance that the decision to reject the null hypothesis is erroneous (i.e., that there really is no difference between the two populations). 

Essentials of Toxic Chemical Risk Science and Society 

---