In research, statistics is defined as a number that represents a property of a sample. It is important to understand statistics and apply it to a research in order to gain an understanding of data. In applying statistics, it should be started with a population or process to be studied.
Population is collection of all things/people/objects that are under study. This population data can be summarized by using descriptive statistics. As an example, there are 9 CDD students participates in Research Method class which consist of 5 females and 4 males. Descriptive statistics will help to organize and summarize that data into: F(5), M(4).
If the population data is over (e.g. 125.000.000 people to be census) and not feasible to be compiled, selected subject of population called a sample is studied. Sample data must be representative of the population to avoid sampling bias – some members of population are more likely to be chosen that other for the sample.
Sampling method is divided into two categories which are representative sampling (probability samples: random sample and stratified sample) and non-representative sampling (non-probability samples: quota sample, purposive sample, and convenience sample). In fact, the best sampling is representative sample because it reduces the possibility of subjectivity and bias.
Inferential statistics is needed to draw conclusions about the entire population through the sample data, accounting for randomness. The findings may based on: hypothesis testing, estimation, associations-correlations, or regression analysis.
Research Methods. (2016). Lecture 3: Intro to Statistics. Sampoerna University