INTRODUCTION
	The aim of this book is to present to statisticians, and to 
statistics instructors, ideas and data relevant to teaching 
statistical inference - especially at the introductory level - 
using resampling methods in addition to, or in place of, 
conventional methods.
	Before proceeding further, here are a problem in 
probability, and one in statistics, to show you what we mean by 
simulation (in probability) and resampling simulation (in 
statistics):
	P:  INSERT ? Three girls  from statbook?
	P:  INSERT ? Bush-Dukakis  from statbook?
	Part I introduces resampling and its teaching to that large 
part of humanity, and even of the statistics profession, that is 
still unaware of resampling.  We begin in Chapter I-1 with the 
results of studies of resampling in the classroom (and a bit of 
data for afterwards, too) from the early 1970s to the present; if 
there were no demonstrated successes or proven superiority over 
the conventional method, there would be no reason to read 
further.  Chapter I-2 then describes the resampling method 
itself.  And Chapter I-3 tells some of the history of the 
development of resampling method.
	Part II discusses methods of teaching statistical inference 
with resampling.  Resampling is best understood by seeing it 
being learned.  Hence Chapter II-1 transcribes an edited taped 
class, to give the sense of the class atmosphere.  Chapter II-2 
discusses the teaching of resampling in a fashion complementary 
with the conventional method and books.  And Chapter II-3 
discusses some of the benefits and costs of teaching the 
resampling method - the spontaneity of the give-and-take between 
students and teachers being both a benefit to students and a cost 
to the teacher because it demands more effort than does a 
standard structured class hour.
	Part III analyses the resampling method from the point of 
view of its effectiveness for users and students, and the nature 
of the cognitive processes involved in carrying out statistical 
inference with resampling and with the conventional method.  
Chapter III-1 looks into the nature of statistical inference to 
ascertain why it is such a difficult subject, and discusses how 
resampling allows the student to focus on the true inherent 
difficulties without getting distracted by unnecessary 
mathematical difficulty and obscurity using the formulaic 
approach.  Chapter III-2 discusses why simulation can sensibly 
attack some problems that the formal sample-space approach cannot 
address.  Chapter III-3 analyses several famous problems and 
shows how the simulation approach that solves them also eases 
problems in statistics.  An afternote shows - half seriously, 
half in jest - how a "try it" simulation approach can hugely 
raise students' IQ (as defined by a newspaper IQ test) in a 
matter of minutes; this typifies the effect of resampling on 
one's intellectual capabilities.
	Part IV takes up some special topics.  Chapter IV-1 
discusses the relationship between mathematicians and the 
teaching of statistics; their love of the esthetics of 
mathematics is a major barrier against teaching the simulation 
approach, which (to mathematicians) lacks the beauty of formal 
equations and proofs.  Chapter IV-2 describes a computer-based 
tutor that uses artificial intelligence to detect whether a 
student's program is correct, and if not, to tell the student 
where errors in logic have been made; the nature of the 
resampling approach uniquely enables the operation of such a 
tutor that works understandably, rather than a mere dumb device 
that finds deviations from the formula that is demanded.  Chapter 
IV-3 discusses the nature of the Resampling Stats program that 
emobdies the resampling approach; it and other parallel languages 
such as Mathematica and APL work in an entirely different fashion 
from Basic and Pascal because they closely mimic the operations 
in simulation.  (Resampling Stats has the additional advantage 
over Mathematica and APL that it is designed only for statistics 
and probability, and therefore is much less difficult to master.)  
	Part V discusses the future of resampling, and the barriers 
it must overcome before it is adopted widely or universally for 
instruction and everyday use - as it surely will be.  Chapter V-1 
discusses students' reactions to conventional statistics teaching 
in the general context of the teaching of mathematics.  And 
Chapter V-II discusses the short-run prospects for resampling 
instruction.  
	These chapters may seem as if they are an argument for the 
use and teaching of resampling methods.  But if that is so, we 
believe, the reason is the characteristics of the resampling and 
the conventional parametric methods, rather than our partiality 
to resampling.  And we do our very best to present all the 
relevant material, pro and con, in as unbiased a manner as 
possible, though we are certainly are adherents of resampling - 
because of its characteristics, we believe.
	The reader who is interested in learning more about the 
practical procedures of resampling methods may consult 
Resampling:  The New Statistics by Julian L. Simon.  And the same 
author's The Philosophy and Practice of Statistics and Resampling 
discusses the philosophical foundations of the subject. 
page # teachbk introtch May 6, 1996