The Different Types Of Statistics That Can Be Used To Describe And Measure Data

There are two primary means of research that include the descriptive statistical method and the inferential method. These two methods encompass the majority of statistical research methods. While both are useful methods they do have benefits and limitations in their use.

Descriptive statistics is a method of analyzing data which presents it in a meaningful way. Through this methodology patterns in data can be seen which allow one to draw conclusions concerning the data or a hypothesis that involves the data. In this way, descriptive statistics can be seen as a means to organize and view data in a manner which is useful to a researcher (AERD, 2014).

Descriptive statistics serve a vital function because raw data is difficult to visualize and this fact is made worse when the volume of data increases. Along with making data meaningful, descriptive statistics also allows for an easier interpretation of the data (AERD, 2014). One of the major benefits of using descriptive statistics is that a distribution or spread of results can be shown through statistics and graphs. This allows data to be seen in a very powerful and simplistic manner.

There are some drawbacks to descriptive statistics. These forms of statistics only take into account quantitative results which do not always tell the whole story (AERD, 2014). For instance, when measuring productivity, the scores could be skewed due to other issues that the testing cannot account for such as poor working equipment or negative workplace culture. For this reason it is important to try to blend some form of qualitative measure into studies.

In contrast to descriptive statistics, inferential statistics provides information about an immediate group of data. For example, the average rate of productivity for a worker could be found from examining a sample of workers. Inferential statistics only refers to a specific group containing data which is known as a population (AERD, 2014). Within this context, inferential statistics draws generalizations concerning a population from which samples are drawn. For example, if 100 auto workers are examined for productivity levels a conclusion can be drawn about all workers in the automotive population. This has the benefit of allowing researchers to draw conclusion based on smaller samples which saves time and resources. However, the downside to this method of research is that it relies on conclusions drawn from a population that has not been entirely measured and tested (AERD, 2014). For this reason, there is always an element of uncertainty in the conclusions.

AERD. (2014). Descriptive and Inferential Statistics. Retrieved from AERD

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