In the beginning, a brief review of the main contributors to

statistical inference, first developed by Sir Ronald Fisher (1890-1962) and after improved in 1933 by Jerzy Neyman and Egon Pearson (2) (to be not confounded with the developer of the well-known Pearson product-moment correlation coefficient, Karl Pearson, who actually was Egon's father), introduces the readers from a historical point of view to what is the principal approach used in rehabilitation research, namely the Neyman-Pearson approach.

However, even if we say it correctly,

statistical inference does not allow us to say very much of value for researchers today.

This was beyond our scope but important to understand for reliable

statistical inference.

Chapter 9-12 discuss

statistical inference, hypothesis testing and ANOVA.

It should be required reading for all statisticians, mathematicians and scientists as it shows how religious beliefs control

statistical inference.

However, despite the numerous books and papers published on the basics of

statistical inference and, thus, on the p-value, there still seems to be a need to highlight what message the p-value exactly contains (and what it does not).

For example, statistical fundamentals (such as the normal distribution and probability) are presented within the context of sampling theory early on in the text, which lays the groundwork for the later chapter on quantitative data analysis, which discusses issues of central tendency, dispersion, and

statistical inference.

The introductory chapter provides the needed background on the characteristics and types of spatial data, and the nature of spatial processes and patterns such as autocorrelation functions and the effects of autocorrelation on

statistical inference.

Scientists and researchers can now perform mass-spectrometry data analysis, perform

statistical inference and prediction, view graphs, and conduct enhanced genomic and proteomic sequence analysis.

Articles in the issue include 1) "Distribution of Clinical Covariates at Detection of Cancer: Stochastic Modeling and

Statistical Inference," 2) "Planning Public Health Programs and Scheduling: Breast Cancer," 3) "Planning of Randomized Trials," 4) "The Use of Modeling to Understand the Impact of Screening on U.

Statistical inference seeks to characterize how sampling variability affects the conclusions that can be drawn from samples of limited size.

The American Statistical Association, 1999; Wilkinson & the Task Force on

Statistical Inference, 1999) currently prevail, coupled with the fact that our knowledge base in the areas of quantitative-based methodology has rapidly expanded in recent years, there is more necessity for students, particularly at the doctoral level, to take more research methods and statistics courses.