Statistical tests are just a way of working out the probability of obtaining the observed or even more extreme variations among samples or between expected and observed value if a specific hypothesis usually the null of no difference is true. Once the probability is known the experimenter can make a decision about the variation, using criteria that are understood and used uniformly (Klimesch, 2006, p 209; Mckillup, 2005).
Chi Square Test:
A statistical test to evaluate variations is referred to as chi square test. The chi square test measures the variations between a statistically produced expected results and an actual outcome to view if there is a statistically essential variations between them i.e. to view if the frequencies observed are essential and it is also the measure of goodness of fit between actual and an expected outcome or collection of outcomes. The expected result is based on statistical process and it denotes the statistical significance notion which is concerned on the probability notions. The formula for chi square test is:
The basic need of any chi square test is that they compare how well an observed breakdown of people over different categories fits some expected breakdown. A chi square test is described as comparing an observed frequency distribution to an expected frequency and then viewing whether that number denotes a greater mismatch that they would expect by chance (Cohen, Manion and Morrison, 2007, p 525; Aron, Aron and Coups, 2007).