One main step for research are the hypothesis tests. Hypotheses are statements that assign variables to cases. A hypothesis performs a number of essential functions. The most important is that it accompanies the guidance of the study. A common problem that appears within the research is the accumulation of interesting information. If the researcher does not manage the strong desire to include extra elements, a study can be weakened by not that important concerns that do not have an answer for the predominant question that is posed. The advantage of the hypothesis is that, if the researchers take it seriously, it reduces what shall be studied and what shall no longer. It differentiates the related and not related facts and in addition, it proposes which is the most right and applicable research method. The ultimate role of the hypothesis is to support a framework as a way to organize the conclusion of the research.

The logic behind the hypothesis testing

In classical tests of significance, there are two kinds of hypothesis used. The first of them is the null hypothesis which says that there is no difference between the parameter and the statistic being in comparison to it. The second one is the alternative hypothesis, which is the opposite of the null hypothesis. It may appear in a number of varieties which rely on the objective of the researcher. The types may be “not the same” (≠), “higher than” (>), or “less than” (<).

If we reject the null hypothesis (finding the statistically important difference), we accept the alternative hypothesis.

Tests of significance: types of tests.

After the assessment of the main types and their assumptions, we will have to choose an appropriate test. There are the two basic classes of significance tests: parametric and nonparametric tests.

Parametric tests

The parametric tests are more effective than the nonparametric tests given that the information that they use are borrowed from interval and ratio measurements. The parametric tests have a few assumptions:

• The observation ought to be independent
• The observations have to be taken from normally distributed populations
• The populations should have equal variance
• The measurement scales must be an interval in order that the arithmetic opera