When it comes to inferential statistics, the idea is to try to reach conclusions that extend beyond the immediate data alone. For example, inferential statistics is used when trying to infer from the sample data what a certain section of the population might think. It may also be used in cases where judgments have to be made of the probability that a difference that is observed between a number of groups is a dependable one or one that might have happened by chance during the course of the study. Hence, inferential statistics can be defined as the process of making inferences from the available data to more general conditions. This can be contrasted with descriptive statistics which is simply used to describe what is going on in the data.
Most of the inferential statistics come from a family of statistical models which is referred to as the General Linear Model. It includes
- The T-test
- Analysis of Variance (ANOVA)
- Analysis of Covariance (ANCOVA)
- Regression analysis
- Factor analysis
- Multidimensional scaling
- Cluster analysis
- Discriminant function analysis, and so on.
Since the General Linear Model is extremely important in the field of statistical analysis, it is probably a good idea to have any serious social researcher familiar with its general workings. Inferential statistics is quite useful and cost-effective since it can make inferences about a population without collecting each and every piece of data.
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