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What Is Inferential Data Analysis?

The Inferential Data Analysis Definition

In the lexicon of statistical terms inferential data is one step beyond. The attempt is to find conclusions that go little bit more than the existing data suggests. It can be used to predict, or infer, what a sample population may think about a particular product or a change in policy. A number of statistical models are used to do inferential data analysis and these can include Analysis of Variance (ANOVA), regression analysis, Analysis of Covariance (ANCOVA) and other possibilities found within the General Linear Model.

statistical data analysis schema

Descriptive and Inferential Analysis of Data

Discussion of inferential analysis of data is often in contrast with descriptive analysis. Inferential data analysis can be used discuss a larger population, but descriptive statistics may only be used effectively with a group being examined; generalization to a larger group is not possible. Descriptive statistics are reviewed with tools such as frequency distribution, central tendency, and will make use of visuals to describe the data. The boundaries are the given population. Descriptive statistics can go no further than the limits of the data set.

These differences do not mean that descriptive data analysis and inferential data analysis cannot be used together. As a matter fact, descriptive and inferential analysis of data can be used as a one – two step movement to get considerable amount of information. Given a small population, descriptive data analysis will bring forth very accurate parameters of that population. When it is time to go one step beyond, and get some idea of what a larger group’s inclinations might be, inferential data analysis can take the earlier results and make accurate guesses.

The Benefits of Inferential Data Analysis

Market research is a major beneficiary of inferential data analysis. In a fast-paced business world corporations do not have the time or resources to collect large samples for analysis. Inferential data analysis permits a representative sample to be used. It is then possible to take the results and use it to infer what the larger consumer population will do. Hypothesis testing can also be done with inferential data analysis. This can allow business to have a better idea of whether or not a notion is going to be well received by the buying public. Given the amount of money that is spent on product development and active marketing, the informed conclusions drawn from inferential statistics can be very helpful. It eliminates the shot in the dark approach to offering new products.

Inferential studies speak in the language of probability. Designing the proper experimental boundaries can make the conclusions extremely reliable. That is perhaps the most important part of any inferential data analysis. The sample must be well constructed and the proper tools be used. Inferential data analysis as well as intelligent data analysis can permit business establishments to move forward with a little more confidence in any marketing campaign. At the same time, it must be remembered that steps forward have to be done with caution. Inferences are highly educated, but there is the chance of an error or sudden changes. Prudence is always recommended when using the findings.

Learn more about descriptive and inferential analysis of data today!