introductory statistics - some misconceptions
DESCRIPTION
Introductory statistics courses often attempt to achieve unrealistic and inappropriate outcomes. Brief PowerPoint offering alternative objectives.TRANSCRIPT
Introductory Statistics:
Dispelling Some
Misconceptions
Misconception No. 1:
The Subject Matter Is Obvious
"To put it simply, all of the hypothesis testing methods taught in a
typical introductory statistics course, and routinely used by applied
researchers, are obsolete; there are no exceptions. Hundreds of journal
articles and several books point this out, and no published paper has
given a counter argument as to why we should continue to be satisfied
with standard statistical techniques. These standard methods include
Student's T for means, Student's T for making inferences about
Pearson's correlation, and the ANOVA F, among others." (Wilcox, R. R. (2002).
Can the weak link in psychological research be fixed? Association for Psychological Science Observer, 15, 11 & 38.)
Message: the question of what one should take
away from a statistics course is not cut and dried.
Misconception No. 2:
You Will Have Data Sophistication
-- A. V. Alexandrov, U. Texas Medical School
-- K. Cobb Sainani, Stanford U.
-- D. Curran-Everett & D. J. Benos, National Jewish Medical & Research Center
Message: even PhD researchers often consult statisticians. Intro stats does not yield expertise.
Misconception No. 3:
Intro Stats Courses Are Effective
-- J. Garfield & D. Ben-Zvi
-- P. L. Gardner & I. Hudson
Misconception No. 4:
Statistics Is About Calculations
Message: there definitely are calculations, but
in everyday situations knowing which ones to
make – and how to use them – is the hard part.
An Alternative Approach to
Statistical Education
• Focus on the important topics.
• Study them from unexpected angles.
• Revisit them until they are familiar.
• Apply them in interesting ways.
• Develop an enjoyment of statistics.