GAISE
Guidelines for Assessment and
Instruction in Statistics Education
COLLEGE REPORT
Robin Lock
Jack and Sylvia Burry Professor of Statistics
St. Lawrence University
rlock@stlawu.edu
GAISE College Group
Joan Garfield
Martha Aliaga
George Cobb
Carolyn Cuff
Rob Gould
Robin Lock
Tom Moore
Allan Rossman
Bob Stephenson
Jessica Utts
Paul Velleman
Jeff Witmer
Univ. of Minnesota (Chair)
ASA
Mt. Holyoke College
Westminster College
UCLA
St. Lawrence University
Grinnell College
Cal Poly San Luis Obispo
Iowa State
UC Davis
Cornell University
Oberlin College
The Many Flavors of
Introductory Statistics
Consumer
Producer
Discipline-specific
General
Large lecture
Year
H.S.
(AP)
Small class
Semester
Quarter
Block
Two year
college
Four year
college
University
Challenge in Writing Guidelines
Give sufficient
structure to provide
real guidance to
instructors.
Allow sufficient
generality to include
good practices in the
many flavors.
Starting point: Cobb Report (1992)
Report from discussions of the Focus Group on
Statistics Education in Heeding the Call for Change
Three Recommendations:
• Emphasize statistical thinking
• More data and concepts, less theory and
fewer recipes
• Foster active learning
Statistically Educated Students
Should believe and understand why…
• Data beat anecdotes.
• Variability is natural and is also predictable
and quantifiable.
• Random sampling allows results to be
extended to the population.
• Random assignment in experiments allows
cause and effect conclusions.
Statistically Educated Students
Should believe and understand why…
• Association is not causation.
• Statistical significance does not necessarily
imply practical significance.
• Finding no statistical significance in a small
sample does not necessarily mean there is no
difference/relationship in the population.
Statistically Educated Students
Should recognize…
• Common sources of bias in surveys and
experiments.
• How to determine the population (if any) to
which inference results may be extended.
• How to determine when a cause and effect
inference can be drawn.
• That words such as “normal”, “random” and
“correlation” have specific statistical meanings.
Statistically Educated Students
Should understand the process through which
statistics works to answer questions. How to…
• Obtain or generate data.
• Graph data as an initial step in analysis.
• Interpret numerical summaries and graphical
displays (answer questions / check conditions).
• Make appropriate use of statistical inference.
• Communicate results of a statistical analysis.
Statistically Educated Students
Should understand the basic ideas of statistical
inference, including the concepts of
• Sampling distribution and how it applies to
making inferences from samples.
• Statistical significance, including significance
level and p-values.
• Confidence interval, including the confidence
level and margin of error.
Statistically Educated Students
Should know
• How to interpret statistical results in context.
• How to read and critique news stories and
journal articles that include statistical
information.
• When to call for help from an experienced
statistician.
Guideline #1
Emphasize statistical literacy and
develop statistical thinking.
Statistical Literacy and Thinking
Statistical literacy: understanding the basic
language and fundamental ideas of
statistics.
Statistical thinking: the processes that
statisticians use when approaching or
solving practical problems.
Suggestions for Teachers
• Model statistical thinking for students.
• Have students practice statistical thinking
(e.g. open-ended problems and projects).
• Let students practice with choosing
appropriate questions and techniques.
Guideline #2
Use real data.
Levels of Reality
Real: Data that were actually collected or
generated to answer some question(s).
Realistic: Hypothetical data with a context
that illustrate a specific point.
Naked: Numbers with no context (and thus
no interest).
Suggestions for Teachers
• Search for raw data from textbooks, software
packages, web data repositories.
• Use summary data from textbooks, articles, and
websites with poll/survey results.
• Get data from class activities and simulations.
• Make larger data sets available electronically. Practice
data entry on small data sets.
• Return to a rich data set at various points in the course.
• Use data with students to answer interesting questions
and generate new questions.
Guideline #3
Stress conceptual understanding rather
than mere knowledge of procedures.
Concepts vs. Procedures
Many (most?) introductory courses contain
too much material.
If students don’t understand concepts,
there’s little value in knowing procedures.
If students do understand concepts,
specific new procedures are easy to learn.
Suggestions for Teachers
• Primary goal is not to cover methods, but to discover
concepts.
• Focus on understanding of key concepts, illustrated by a
few techniques, rather than a multitude of techniques
with minimal focus on underlying ideas.
• Pare down content to focus on core ideas in more depth.
• Use technology for routine computations, use formulas
that enhance understanding.
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Guideline #4
Foster active learning in the classroom.
Types of Active Learning
Group or individual: problem solving,
exploratory activities and discussion
Lab activities: physical and computer-based
Demonstrations: based on “live” results
from students or software
Suggestions for Teachers
• Ground activities in the context of real problems.
• Intermix lectures with activities and discussions.
• Precede computer simulations with physical
explorations.
• Collect data from students.
• Encourage predictions from students anticipating
statistical results.
• Plan sufficient time to do and wrap up the activity.
• Provide lots of feedback and assessment.
Guideline #5
Use technology for developing concepts
and analyzing data.
Types of Technology
Graphing calculators
Traditional statistical packages
Conceptual statistical software
Educational support
Applets
Spreadsheets
Suggestions for Teachers
•
•
•
•
•
•
•
Access large real data sets.
Automate calculations.
Generate and modify appropriate statistical graphics.
Perform simulations to illustrate abstract concepts.
Explore “what happens if...” scenarios.
Create reports
Consider
–
–
–
–
Ease of data entry, ability to import data
Interactive capabilities
Dynamic linking between data, graphs, numerics
Ease of use and availability
Guideline #6
Use assessments to improve and
evaluate student learning.
Types of Assessment
Homework
Quizzes and exams
Projects
Activities
Presentations
Lab reports
Minute papers
Article critiques
Class discussion/participation
Suggestions for Teachers
• Integrate assessment as an essential (and current)
component of the course.
• Use a variety of assessment methods.
• Assess statistical literacy (e.g. by interpreting or
critiquing articles and graphs in the media).
• Assess statistical thinking (e.g. by doing student
projects or open-ended investigative tasks).
• For large classes
– Use group projects instead of individual
– Use peer review of projects
– Use multiple choice items that focus on choosing
interpretations or appropriate statistical approaches.
Six Recommendations
1. Emphasize statistical literacy and
develop statistical thinking
2. Use real data
3. Stress conceptual understanding rather
than mere knowledge of procedures
4. Foster active learning
5. Use technology to develop conceptual
understanding and analyze data
6. Use assessments to improve and
evaluate learning
Making It Happen
Evolution through small steps:
•Find/develop a case study of statistical interest
•Find a new real data set
•Delete a topic from the list you currently cover
•Have students do a small project or new activity
•Integrate a neat applet into a lecture
•Try some new types of quiz/exam questions