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What is the purpose of inferential statistics?

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Inferential statistics is a branch of statistics that can be used when researchers and mathematicians want to attempt to extrapolate on and reach conclusions that extend beyond the raw data itself. While a study only uses a small sample of the total population it is studying, inferential statistics can be used to attempt to estimate traits and thought patterns of behavior among the population as a whole.

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The methods derived in inferential statistics are used by researchers and scientists who want to try and understand the larger groups they are studying as a whole. These often have a reach that extends beyond the population samples that had tests performed on them or answered surveys. Many statisticians do not want to deliver information about the studied sample alone, they want to provide extrapolations on the behavior of the population as a whole. There are a number of different specific statistical analysis tools used by inferential statisticians in this process.

Most of the modeling and algorithms used in inferential statistics come from a group of different statistical models called the General Linear Model. This also includes different multivariate analysis tools. When combined with the various other tests used in statistics, these models can help paint a picture of population behavior and attributes.

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