Once the data analyzing process is complete, the researcher is ready to test the hypothesis, which was formulated earlier. Hypothesis testing involves systematic methods, which are used to evaluate the data and aid the decision-making process. Various statistical measures are used to test the data: parametric analysis (Ttest, ANOVA, Regression, etc.) and non-parametric analysis (ChiSquare, Kruskal Wallis, Mann“Whitney, etc.). These testing methods differ depending on the types of measurements and tools used for those measurements. One must choose the appropriate statistical method in order to obtain meaningful results. Based on the results of the calculations, hypothesis testing will result in either the hypothesis being accepted or rejected. The hypothesis is ruled out or modified if its predictions are incompatible with the experimental tests.
Generalizations and Interpretation
This is the final stage of the scientific research process. While testing, if the hypothesis is upheld several times, the researcher may form generalizations, i.e., build theories based on it. Generalizations give an indication of the actual achievement of the researcher. In case, the researcher had no hypothesis while starting the experiment he tries to explain his findings on the basis of some already established theory. This is known as interpretation. It is a process, which makes it easier to understand the factors that explain what was observed by the researcher during the course of his study. It also provides a theoretical conception, which serves as a guide. Interpretation may lead to new questions, thus leading to further researches.