The hypothesis is directly related to a theory but contains operationally defined variables and is in testable form. Hypotheses allow us to determine, through research, if our theory is correct. In other words, does prior work experience result in better grades? When doing research, we are typically looking for some type of difference or change between two or more groups. In our study, we are testing the difference between having work experience and not having work experience on college grades. Every study has two hypotheses; one stated as a difference between groups and one stated as no difference between groups.
When stated as a difference between groups, our hypothesis would be, “students with prior work experience earn higher grades than students without prior work experience.” This is called our research or scientific hypothesis. Because most statistics test for no difference, however, we must also have a null hypothesis. The null hypothesis is always written with the assumption that the groups do not differ. In this study, our null hypothesis would state that, “students with work experience will not receive different grades than students with no work experience.”
The null hypothesis is what we test through the use of statistics and is abbreviated H0. Since we are testing the null, we can assume then that if the null is not true then some alternative to the null must be true. The research hypothesis stated earlier becomes our alternative, abbreviated H1. In order to make research as specific as possible we typically look for one of two outcomes, either the null or the alternative hypothesis. To conclude that there is no difference between the two groups means we are accepting our null hypothesis. If we, however, show that the null is not true then we must reject it and therefore conclude that the alternative hypothesis must be true. While there may be a lot of gray area in the research itself, the results must always be stated in black and white. More on hypothesis testing will be discussed in chapter 9.