Difference between Descriptive Statistics and Inferential Statistics

Descriptive Statistics

Descriptive statistics includes statistical procedures that we use to describe the population we are studying. The data could be collected from either a sample or a population, but the results help us organize and describe data. Descriptive statistics can only be used to describe the group that is being studying. That is, the results cannot be generalized to any larger group.

Descriptive statistics are useful and serviceable if you do not need to extend your results to any larger group. However, much of social sciences tend to include studies that give us “universal” truths about segments of the population, such as all parents, all women, all victims, etc.

Frequency distributions, measures of central tendency (mean, median, and mode), and graphs like pie charts and bar charts that describe the data are all examples of descriptive statistics.

Inferential Statistics

Inferential statistics is concerned with making predictions or inferences about a population from observations and analyses of a sample. That is, we can take the results of an analysis using a sample and can generalize it to the larger population that the sample represents. In order to do this, however, it is imperative that the sample is representative of the group to which it is being generalized.

To address this issue of generalization, we have tests of significance. A Chi-square or T-test, for example, can tell us the probability that the results of our analysis on the sample are representative of the population that the sample represents. In other words, these tests of significance tell us the probability that the results of the analysis could have occurred by chance when there is no relationship at all between the variables we studied in the population we studied.

Descriptive vs. Analytical approach to research

. A descriptive approach to research is called as a foundation for research. Its logic is based on the statistics of the research analysis. So, the descriptive research can’t take into account the validity of the research results, because it does not explain the causes of the result.

On the other hand, Analytical approach is concentrates on the process of the final result rather giving importance to the result. Analytical approach stands applicable in all stages of research, right

from the articulation of thesis to the formulation of arguments on the issues mentioned in the research.

The descriptive research method is basically divided into three types-

• Observation Method• Case Study Method• Survey Method

The Observation Method concentrates on observing the subject in both Natural and Laboratory methods to draw the conclusions on the research. Its highly useful in the Natural observation because the original results of the research can be obtained. In laboratory method the result would be according to the quantities of the data provided to the observation. Case study method involves a deep research on the problems discussed. Survey method is based on the questionnnaire prepared for the participants. After participants answer the questionnaire, the research moves towards its final stage.

There are different types of Analytical research methods available. They are,

• Regression Analysis approach• Grouping Analysis approach• Multiple Equation Methods

In Regression Analysis the outcome, or the result produced from the research, is affected by the one or more individual elements of the experiment. Therefore, the Regression analysis method is further divided into four major subdivisions – Logistic, Linear hierarchal, Duration, Ordinary least squares (OLS) method.

Grouping methods of analytical approach are based on classification and grouping of the variables in an experiment based on their discriminate values and characteristics. Multiple method is just an extension of Regression method discussed above. This method

explains about the path of individual elements in an experiment. There are two main types of multiple equation models – 1) Path analysis 2) SEM-structural equation models.