Hypothesis testing is a cornerstone of empirical reasoning as it relates to using inferential statistics
The first step in hypothesis testing is to state the null hypothesis. The null hypotheses states that there is no difference or association between the phenomena or variables in the population of interest. It could also be stated as meaning that a treatment will have no effect on a population.
The second step is to state the alternative or research hypothesis. This is the whole reason for conducting a research study. Researchers believe, through a review of the literature and your clinical expertise, that there is going to be a difference or association between the variables. Or, they hypothesize that the treatment will have some sort of effect on members of the population.
The third step is to set the criteria for making a decision to either "do not reject" or "reject" the null hypothesis based on the observed values in the sample. This is called the alpha level and it is defined as the risk that researchers are willing to take to have a Type I error (false positive). It is also known as the level of significance that needs to be achieved in order to assume significant effects of associations between variables in inferential statistics. Most alpha values are set at .05. The area below the value of .05 is called the critical area and denotes statistical significance.
The fourth step is to collect sample data and run inferential statistics in order to either "do not reject" or "reject" the null hypothesis. If the statistical test yields a p-value BELOW .05, then it is statistically significant and researchers "reject" the null hypothesis. If the p-value is ABOVE .05, then it is not statistically significant, and researchers "do not reject" the null hypothesis.
Components of hypothesis testing
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