There are two types of hypotheses:
There are two types of hypothesis testing errors:
It is important to note that sample size plays an important role in determining statistical significance. When the sample size is too small, there may not be enough observations to be able to determine whether there is a statistically significant difference or relationship between the variables. So, too small of a sample size increases the risk of a type 2 error and incorrectly accepting the null hypothesis when there actually is a difference or relationship present. Small sample size is a common problem in student projects, so it is important to be aware that the failure to find a statistically significant difference may be due to small sample size (lack of statistical power).
Statistical Power is the ability to discern a deviation from the null hypothesis. In short, a researcher who wants adequate statistical power must enlist a minimal number of participants for their particular study. There are many ways to calculate the minimal number of participants. One tool is G*Power: