Descriptive and inferential statistics

The relationship that exists between inferential versus descriptive statistics is important in explaining and describing data. This paper aims at defining the two aspects of interpreting data and bringing out the difference in the two.

Descriptive statistics is based on summarizing data in a way that enables a particular pattern to emerge from the data (Asadoorian & Kantarelis, 2005). Its main purpose is to describe data. This means that reaching conclusions from data analyzed through descriptive statistics is not possible. However, it enables a meaningful way of presentation data, for easy interpretation. Two types of statistic are used in descriptive statistics. The first one represents measures of central tendency. These include the mean, the mode and the median. They enable the statistician to know the central position of data in a frequency distribution. The second type is the measures of spread. They help the statistician to know how the data is spread in a set of distribution. An example is the standard deviation.

Inferential statistics, on the other hand, operates on samples but is interested in getting information about whole populations. Samples are used to represent the population, and inferences are made about the population as per the interpretation of the sample’s population (Asadoorian & Kantarelis, 2005). It applies the process of sampling that is characterized by sampling error. Because of the sampling error, the sample may not fully represent the population. This leads to the other characteristic of inferential statistics, in that it operates from assumptions. Educated guesses are also made. Inferential statistics has two methods which include testing of the statistical hypothesis, and the estimation of the parameters.