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Inferential Statistics

Data Analysis: Analyzing Data - Inferential Statistics
Inferential statistics deal with drawing conclusions and, in some cases, making predictions about the properties of a population based on information obtained from a sample. While descriptive statistics provide information about the central tendency, dispersion, skew, and kurtosis of data, inferential statistics allow making broader statements about the relationships between data. Inferential statistics are frequently used to answer cause-and-effect questions and make predictions. They are also used to investigate differences between and among groups. However, one must understand that inferential statistics by themselves do not prove causality. Such proof is always a function of a given theory, and it is vital that such theory be clearly stated prior to using inferential statistics. Otherwise, their use is little more than a fishing expedition. For example, suppose that statistical methods suggest that on average, men are paid significantly more than women for full-time work. Several competing explanations may exist for this discrepancy. Inferential statistics can provide evidence to prove one theory more accurate than the other. However, any ultimate conclusions about actual causality must come from a theory supported by both the data and sound logic.

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The following briefly introduces some common techniques of inferential statistics and is intended as a guide for determining when certain techniques may be appropriate. The techniques used generally depend on the kinds of variables involved, i.e. nominal, ordinal, or interval. For further information on and/or assistance with a given technique, refer to the books and in-house support listed in the Resources section at the end of this module. + Chi-square (2 2) tests are used to identify…...

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