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In a research study, the most effective ways to solve a problem is to form the hypothesis and test it. Here the the steps to effectively form a hypothesis and test it for validity.
1. Identify the null hypothesis for both one-tailed and two-tailed tests. The null hypothesis is a parameter equals zero although there are exceptions. A null hypothesis is u1-u2 =0 or u1= u2. For a one tailed test , the null hypothesis is either a parameter is greater than or equal to zero.
2. The second step is to specify the α level that is known as the significance level. The values are 0.05 and 0.01.
3. The third step is to compute the probability value ( p-value). The p-value is the probability of obtaining a sample statistic as different or more different from parameter specified in the null hypothesis given that the null hypothesis is true.
4. The last step is to compare the probability value with the α level. If the probability value is lower than the α level, then you reject the null hypothesis. The lower the probability value ( p-value), the more confidence you will have that the null hypothesis is false. If your p-value is higher than the conventional α level of 0.05, then most scholars will consider your findings inconclusive. For example, if the p-value is 0.005, this p-value is lower than your α level of 0.05. Therefore, you would reject the null hypothesis, and is more confidence that the null hypothesis is false. If you fail to reject the null hypothesis , that does not mean that you will support for the null hypothesis. It just means that you do not have sufficiently strong data to reject it.
Simply, to make the right decision, you need to form the null hypothesis and alternate hypothesis. If your p-value is smaller than the α level, then you reject the null hypothesis and accept the alternate hypothesis. If the p-value is greater than 0.05 α level, then you do not reject the null hypothesis.
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