There are a few standard steps in hypothesis testing:
- State the hypothesis in general terms
- Exploratory approaches.
- Intentional search approaches.
- Extending-coupling approaches.
- Operationalize the hypothesis:
- What will be measures/observed? (Dependent variables)
- What will be manipulated? (Independent variables)
- How are these tied to the hypothesis?
- What methods will be employed?
- How will you test your hypothesis?
- How will the relation between your independent and dependent variables be examined?
- What results do you anticipate?
- How will the results provide evidence for your hypothesis
It is also important to note that a hypothesis is never really proved or disproved. You provide evidence either in favor of the hypothesis or evidence that casts doubt on the hypothesis. Thus, empirical evidence never proves the hypothesis but rather “establishes” the hypothesis for acceptance. As such, the hypothesis is never proven as a logical consequence of empirical evidence. A core tenet of the scientific process and theory building is falsifiability.
Furthermore, statistical tests aid in making inference from the observed sample to unobserved population. That is, they let you evaluate the population indirectly. One can only go from the denial of S (sample) to the denial of P (population); and not from the assertion of S to the assertion of P. Statistical tests also allow making statements of reliability of the measure.