First, this section discusses whether rejection of the null hypothesis should be an all-or-none proposition. Then, it discusses how to interpret non-significant results; for example, it explains why the null hypothesis should not be accepted or should be accepted with caution. It also describes how a non-significant result can increase confidence that the null hypothesis is false.

Interpreting Non-Significant Results


  1. You are unable to reject the null hypothesis or accept the alternative hypothesis if your p value is .13. However, you cannot conclude that the null hypothesis is true either. Thus, you only fail to reject the null hypothesis.

  2. Although you were unable to reject the null hypothesis here, you did find a difference in your sample. Because of this sample difference, you can now be more confident that the population difference does really exist, and doing further research is the best way to find out. You definitely do not accept the null hypothesis.