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.