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TABLE OF CONTENTS:
What are the above Web sites about?
Descriptive statistics is a branch of statistics which deals with collecting, simplifying, and obviously describing data. The major goal of descriptive statistics includes organization, summary and trace out of data, all to make data more understandable and comprehensible. An approach often used in descriptive statistics is a collection of statistical techniques called exploratory data analysis or EDA. For instance, suppose that we have a rather large data set of all GPA scores of the Montclair State University students. If we only take a general look at the entire data set, chances are we will not learn much from it. However, if we calculate the average of all scores, then we will learn something about these scores. Generally speaking, it is wise to explore data before a statistical analysis to gain deeper insight into the nature of the data. Go to EDA Web Page to learn more about this approach.
Inferential statistics is another branch of statistics which involves drawing conclusions about a population based on the statistical analysis of a sample. Inferential statistics is concerned with evidence provided by a sample for either truth or falseness of a specific claim made about a population. The methods used by this branch allow us to make judgments (or inferences) about a population based on the properties of a sample. A good example of inferential statistics usage would be the election poll. Statisticians pick a sample of voters from all citizens and make predictions about the future outcome. It is very important mentioning here that the sample must always be representative of the population it comes from.
Go to Sampling Strategies Web Page if you want to learn more about sampling designs.
Have a question or a comment? Contact scp@stat.montclair.edu.
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