Inferential hypothesis testing, error

The need to control the experiment-wise error rate may not apply to exploratory analyses. Statisticians often perform formal statistical tests for exploratory purposes. So, no formal hypotheses are stated and no inferences are made based on them. Even though the act of performing formally an exploratory test involves the same steps as inferential testing, it is conceptually different because of the absence of a null hypothesis. The p-value obtained in such a test should be viewed as a measure of the level of inconsistency of the data with the underlying assumptions of the test rather than error probabilities involved in making causal inferences. 

Flowever, circumstances arise where one may desire to use modeling for confirmatory analyses, as will be discussed later. Analyses of this type are hypothesis confirming, which are inferential in nature. If multiple tests are conducted, adjustment usually must be made to prevent inflation of against Type I error. Therefore, for modeling to be used in confirmatory analyses, special care must be taken to protect against Type I error. Our purpose is to draw attention to this issue, through discussion of two separate application areas in bioequivalence. 

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