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Five Steps of Hypothesis Testing
The basic logic of hypothesis testing is to prove or disprove the research question. By only allowing an error of 5% or 1% and making correct decisions based on statistical principles, the researcher can conclude that the result must be real if chance alone could produce the same result only 5% of the time or less. These five steps consists of all the decisions a researcher needs to make in order to answer any research question using an inferntial statistical test.
1. STATING THE RESEARCH QUESTION.
The first step is to state the research problem in terms of a question that identifies the population(s) of interest to the researcher, the parameter(s) of the variable under investigation, and the hypothesized value of the parameter(s). This step makes the researcher not only define what is to be tested but what variable will be used in sample data collection. The type of variable (or combination of variables as in relationship type research questions) whether categorical, discrete or continuous further defines the statistical test which can be performed on the collected data set.
For example:
Is the mean first salary of a newly graduated student equal to $30,000?
The population of interest is all students who have just graduated. The parameter of interest is the mean and the variable salary is continuous. The hypothesized value of the parameter, the mean, is $30,000. Since the parameter is a population mean of a continuous variable variable, this suggests a one sample test of a mean. 2. SPECIFY THE NULL AND ALTERNATIVE HYPOTHESES.
The second step is to state the research question in terms of a null hypothesis (H0) and a alternative hypothesis (HA). The null hypothesis is the population parameter, µ = $30,000 (H0: µ = $30,000). The alternative hypothesis is the population parameter does not equal $30,000 ( HA: µ

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