Descriptive and Inferential Statistics: What is the difference?

In a situation when data has to be analysed, like for instance, the marks achieved by a certain number of students for a certain piece of course work, it is possible to put to use descriptive as well as inferential statistics in the analysis of the marks that is to be done. Mostly, in all the researches that are conducted on the groups of people, both descriptive as well as inferential statistics are put to use so as the results can be analysed and conclusions can be drawn. Now, we need to understand what is the real difference between descriptive and inferential statistics?

Descriptive Statistics

It is the term that is given to that type of data analysis which helps to describe, show or help to describe and summarize the data in such a way that meaning is added to it. However, the descriptive statistics do not offer the scope for deriving conclusions beyond the data analysis and help in concluding the acceptance or rejection of the hypotheses. They are only the simpler ways to describe the data. Descriptive statistics play a very important role because if the data is represented otherwise it would be difficult to understand the representation of the data, More, so if there was a lot of data. Generally, there are two types of statistics that are used for describing the data.

1. Measures of Central Tendency: it helps to measure the central position of a frequency distribution for a group of data.
2. Measures of Spread: it helps to measure how spreads out the scores of the data ar

Inferential Statistics

While Descriptive statistics give information about an immediate group of data inferential statistics are those techniques that help us to make use of these samples so as to make generalisations regarding the population from which the samples have been extracted. It is necessary that the sample represents the population accurately. The method for achieving the same is called sampling. There is a fact about sampling that causes the occurrence of Inferential Statistics and that is the occurrence of sampling error and the sample does not represent the population perfectly. The methods of inferential statistics are

1. Estimation of parameters
2. Testing of statistical Hypotheses

The word “Mobile” is the most commonly used word we see around. It is able to put research in a context which has not been understood before. It provides a whole lot of advantages that researchers have been seeking for ages in order to bring in the accuracy in their analysis and better responsiveness. It brings in the idea of just in time feedback and brings in the synergy between the respondent and the researcher even if they are at different locations. Of course, mobile research has grown but it is thought provoking that why it has not boomed?

Research a has revealed that mobile device surveys are on a constant rise but at the same time the dropout rates for smartphones are almost twice. Respondents have been seen using mobile devices as medium for easy participation in research. However, a significant barrier in mobile research completion is the difference in the screen size and the variation in the ease of data entry. It is important in this phase that the screen size, the functions of data entry and the length of the survey should have the flexibility to fit into the situation of the respondent and not just the need of the respondent.

The research fraternity needs to appreciate the ease and comfort with which the respondents use the smart phone devices. In this learning process of smart phone adaptability in research the researchers are learning to overcome the barriers and finding means to squeeze out meaning and value out if shorter mobile texts. There are certain tips that researchers could follow in order to stay ahead in this trend:

1. Do not resist but respond to change
2. Customize and offer alternatives for technology involved research by giving options for desktops/mobiles and smart phone alternatives.

The feasibility of technology is all about the tipping points. In time to come, the mobile access of research would actually reach a point where the researchers would have  no choice  but to accept the change. The question that is triggered in each one’s mind is whether research would be able to accept and adapt to the methods and acceptations that are changing or they would remain stuck with the expected but unacceptable dropout rates?