The purpose of the article is to demonstrate the need of a better understanding of statistics especially inferential statistics. The article demonstrates the purpose of knowing what it is, what can be done with its knowledge, and how students, librarians and information scientists can benefit from it. The article demonstrates the use of its knowledge, it's a simple method of comparison and it is very useful for students. It states that library schools should teach and offer this course for students and how they can benefit from its knowledge.
The article describes what inferential statistics is. Its methods serve the purpose of describing measurable characteristics of some set of entities like people. Statistical inference is inference about a population from a random sample drawn from it or, more generally, about a random process from its observed behavior during a finite period of time. It includes: point estimation, interval estimation, null hypothesis and prediction.
There are several distinct schools of thought about the justification of statistical inference.
With inferential statistics, we are trying to reach conclusions that extend beyond the immediate data alone. For instance, we use inferential statistics to try to infer from the sample data what the population might think. We can use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study. Therefore, we use inferential statistics to make inferences from our data to more general conditions; we use descriptive statistics simply to describe what's going on in our data and I will describe it later on.
The purpose is to demonstrate that inferential statistics can be of great use and libraries and information science are already putting it to use. Data is available on the use of this...