Quantitative research is influenced by the empiricist paradigm, which means that it is concerned with cause and effect of social phenomena and uses the data – which is based on empirical observation and their critical interpretation. Qualities of quantitative research include: Deductive, Begins from theory – i.e. it is established to test theory and Can be used to make generalizations and / or to test hypotheses. Quantitative studies provide data that can be expressed in numbers—thus, their name. Because the data is in a numeric form, we can apply statistical tests in making statements about the data. These include descriptive statistics like the mean, median, and standard deviation, but can also include inferential statistics like t-tests, ANOVAs, or multiple regression correlations. Statistical analysis lets us derive important facts from research data, including preference trends, differences between groups, and demographics. Quantitative studies’ great strength is providing data that is descriptive—for example, allowing us to capture a snapshot of a user population. Basically, statistical significance of quantitative data tells you whether your findings are real, while effect size tells you how much they matter. If you are able to achieve statistical significance with a smaller sample size, the effect size is fairly substantial. It is important to take both statistical significance and effect size into account when interpreting your data.