Researchers often report quantitative observations in the form known today as statistics. The term statistics derived as a form of mathematics in the UK and dates to the 17th century (Porter, 1986). The use of statistics started in politics mostly to understand injustices, inequalities, and inefficiencies of political opponents, providing a way to gain political advantages. In social science, statistic results derives from techniques associated with population and sampling. Noting that population is the whole and a sample is a portion of the whole that fully represents all parts of the whole. One purpose for conducting quantitative research is to use a representative sample from a population to infer that any results found in the sample as happening in the population.

Within the techniques of statistical analysis, there are associating terms such as standard error (S.E.), confidence interval (CI), probability values (p-value) and so on. All the terms are part of any complex statistical model used to determine if a hypothesis is true or false, and to support the decision. Although using statistical models is a vital tool in social science, some techniques are under scrutiny from governing intuitions, such as the APA for abuse.

There are discussions happening among researchers today indicating the abuse of statistics in terms of approaches, misrepresenting results, confidence intervals, and so on. Student researchers and professionals need to understand some of the inherent flaws and benefits associated with each statistical technique when interpreting statistical results. In future blog posts, discussions centered on some of these topics helps to increase awareness for those who want to make the best decisions based on quantitative research and statistical analyses.