Quantitative research is one as the name suggests is a research made with the help of numerical facts and figures. Statistical analysis is an important part in quantitative research approach. Statistical analysis is applied on the collected data in order to test a hypothesis and make an inference. This essay gives an overview of significance of statistical testing, chi square Test, reliability statistics, correlation and dependence, null hypothesis, alternate hypothesis, test statistics and rejection region.
Significance of Statistical tests
Cox, Hand and Herzberg (2005, p 47) defines that the statistical methods for the data analysis can be categorized as probabilistic or as descriptive. The former method consists of graphical display and tabulation of great importance both in presentation of conclusion and in detailed analysis. Probability enters in statistical analysis in different ways. The most obvious is that one identifies explicitly that conclusions drawn from limited data are uncertain and that therefore it is a better thing to control and measure probabilities drawing the incorrect conclusions partly but by no means solely as a precaution against over interpretation. The statistical significance test is the most widely referred component based with such probabilities. In order to calculate whether some variations are significant or not statistical tests required to be applied. Statistical significance tests estimate some confidences interval around some estimated mean value. The confidence interval has been derived analytically for coherence and spectra.