In simple terms, with hypothesis testing, we are always trying to come to a conclusion about a mean of a single variable or about the means of two variables. For instance, for hypothesis testing with one variable, you might test what the mean temperature of a healthy individual is (a study conducted by Alan Shoemaker). The null hypothesis (H0) might be, "The mean temperature of a healthy individual is 98.6 degrees Fahrenheit." The alternate hypothesis (H1) would then be that the mean temperature of a healthy individual is NOT 98.6 degrees Fahrenheit. In Alan Shoemaker's study, he tested 130 healthy individuals and found that in 62% of cases, the temperature was less than 98.6. The result was significant enough to reject the null hypothesis.

For hypothesis testing with two variables, we might be interested in knowing if there is a difference in the mean value of properties sold by male real-estate agents and female real-estate agents in a given area of the country. The null hypothesis (H0) would be that there is no difference in the mean values of properties sold. Usually, the researcher is hoping to reject the null hypothesis, meaning that there is a difference between the mean price of properties sold by male real-estate agents as compared to properties sold by female real-estate agents. To determine whether or not to reject the null hypothesis, a significance test is conducted to determine if the results warrant rejecting the null hypothesis.