Inference inferential statistics

Two very different approaches to inferential statistics exist the classical or fre-quentist approach and the Bayesian approach. Each approach is used to draw conclusions (or inferences) regarding the magnitude of some unknown quantity, such as the intercept and slope of a dose-response model. The key difference between classical... 

Up to now we have been discussing descriptive statistics. Inferential statistics uses statistical techniques to make inferences about wider populations from that from which our data are drawn. This involves making estimates and hypothesis testing. 

Inferences about a population are made on the basis of a sample taken from that population. The process of inferential statistics requires ... 

Unpredictable variability in individual responses, coupled with the need to forecast the aggregate responses of an entire population of future subjects, provide the reasons why biostatisticians are involved in clinical trial design and analysis. Inferential statistics is the discipline of making inferences about populations by analyzing data from samples that were drawn from those populations in a prescribed way. If we could somehow look into a crystal ball and measure the actual future responses to a new drug from the entire... 

Statistics is a collection of methods of enquiry used to gather, process, or interpret quantitative data. The two main functions of Statistics are to describe and summarize data and to make inferences about a larger population of which the data are representative. These two areas are referred to as Descriptive and Inferential Statistics, respectively both areas have an important part to play in Data Mining. Descriptive Statistics provides a toolkit of methods for data summarization while Inferential Statistics is more concerned with data analysis. 

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. 

D. B. Rubin first suggested PPC in 1984 (31) as a tool for constructing inferential procedures in modem statistical data analysis. In this approach a model is estimated directly from the index data, and then a new set of data is generated through the simulation of the resulting model. The simulated data set is compared with the index data to see if the model s deficiencies have a noticeable effect on the substantive inferences (9). The basic approach for PPC within the context of PPK modeling is as follows ... 

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