id: 05705197
dt: j
an: 2010c.00437
au: Ernst, Michael D.
ti: Teaching inference for randomized experiments.
so: J. Stat. Educ. 17, No. 1, 8 p. (2009).
py: 2009
pu: Taylor \& Francis, Abingdon, Oxfordshire; American Statistical Association
(ASA), Alexandria, VA
la: EN
cc: K75
ut: introductory courses; textbooks; statistics; statistical inference;
sampling; probability; data collection
ci:
li:
ab: Summary: Nearly all introductory statistics textbooks include a chapter on
data collection methods that includes a detailed discussion of both
random sampling methods and randomized experiments. But when
statistical inference is introduced in subsequent chapters, its
justification is nearly always based on principles of random sampling
methods. From the language and notation that is used to the conditions
that students are told to check, there is usually no mention of
randomized experiments until an example that is a randomized experiment
is encountered, at which point the author(s) may offer a statement to
the effect of "the randomization allows us to view the groups as
independent random samples." But a good student (or even an average
one) should ask, "Why?" This paper shows, in a way easily accessible to
students, why the usual inference procedures that are taught in an
introductory course are often an appropriate approximation for
randomized experiments even though the justification (the Central Limit
Theorem) is based entirely on a random sampling model. (Contains 7
figures.) (ERIC)
rv: