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# An Active Approach to Statistical Inference using

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```									An Active Approach to
Statistical Inference using
Randomization Methods
Todd Swanson & Jill VanderStoep
Hope College
Holland, Michigan
Outline

   Background
   Content
   Pedagogy
   Example
   Assessment
   Future
Inspiration

“Our curriculum is needlessly complicated
because we put the normal distribution, as an
approximate sampling distribution for the
mean, at the center of the curriculum, instead
of putting the core logic of inference at the
center.”
George Cobb (USCOTS 2005)

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Previous Work
Concepts of Statistical Inference:
A Randomization-Based Curriculum
   An NSF funded project in which modules
were developed to teach inference through
randomization techniques.
   Principle Investigators: Allan Rossman and
Beth Chance (Cal Poly)
   Work done in 2007-08

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Development of Text
An Active Approach to Statistical Inference
   Along with Nathan Tintle, we
developed first draft of a text
in 2009
   Used the modules developed
by Rossman and Chance as
the base
   First used at Hope College in
the Fall of 2009

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Development of Text

   Revisions were made during summer 2010.
   This fall we have joined up with Allan
Rossman, Beth Chance and Soma Roy (all of
Cal Poly) and George Cobb (Mt. Holyoke) to
continue to make significant revisions to our
materials.

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Content
   We begin with inference on the first day of
the course and teach it throughout the entire
semester
   First half of course is based on randomization
methods and second half is based on

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   Chapter 1: Introduction to Statistical Inference:
One proportion
   Flipping coins and applets are used to model the null
and their results are used to determine p-values.
   Chapter 2: Comparing Two Proportions:
Randomization Method
   Explanatory and response variables are introduced
   Permutation tests are introduced
   First by using playing cards then with Fathom
(perhaps applets in the future)
   Observational studies/experiments
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   Chapter 3: Comparing Two Means:
Randomization Method
   Permutation tests of means with cards and Fathom
   Type I and type II errors introduced
   Chapter 4: Correlation and Regression:
Randomization Method
   Scatterplots, correlation, and regression are reviewed
   Permutation tests are used to test correlation

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    Chapter 5: Correlation and Regression: Revisited
   Sampling distributions are used to model scrambled
distributions
   Confidence intervals (range of plausible values)
   Power is defined and students explore how it relates to
sample size, significance level, and population
correlation
    Chapter 6: Comparing Means: Revisited
   Standard deviation, normal distributions, and t-
distributions
   The independent samples t test is introduced
   Confidence intervals and power
   Paired-data t test and ANOVA are also introduced
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    Chapter 7: Comparing Proportions: Revisited
   Power is explored in relationship to the difference in
population proportions, sample size, significance level,
and size of the two proportions
   The chi-square test for association is introduced
    Chapter 8: Tests of a Single Mean and Proportion
   Single proportion: binomial, normal distributions, and
confidence intervals
   Single mean: t-test and confidence intervals
   Chi-squared goodness of fit test

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Main differences between our randomization

   Traditional method of teaching introductory
statistics:
   Descriptive statistics
   Probability and sampling distributions
   Inference

   Randomization method
   Inference on day one

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Main differences between our randomization
   Most of the time we visit and re-visit the core-
logic of statistical inference as first
demonstrated by randomization methods.
   We spend limited time teaching descriptive
statistical methods and instead include time
to review and reinforce the proper use of
descriptive statistical methods through
hands-on real data analysis experiences.
   We eliminate the explicit coverage of
probability and sampling distributions.
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Main differences between our randomization
   We present an intuitive approach to power by
looking at the relationships between power and
sample size, standard deviation, difference in
population proportions or means, etc. We think this
helps students better understand the core logic of
statistical inference.
   Confidence intervals are presented after tests. We
demonstrate how tests of significance can be used
to create ranges of plausible values for the
population parameter.

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Pedagogy

   Topics are introduced through a brief lecture
   Students work on activities to learn and
reinforce the topics.
   Tactile learning (shuffling cards and flipping coins)
to estimate p-values
   Computer based simulations
   Collecting data and running experiments

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All our classes meet in a computer classroom.

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Real Data --- Real Research

   We try to avoid cute, but impractical
illustrations of statistics. We include real data
and research that matters.
   Homework problems and case-studies also
involve real statistical data and research.
   Each chapter contains a research paper that
students read and respond to questions.
   Students complete in-depth projects where
they design a study, collect data, and present
their results in both oral and written form.
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Example: Bob or Tim?

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Tim or Bob?

   A study in Psychonomic Bulletin and Review (Lea,
Thomas, Lamkin, & Bell, 2007) presented evidence
that “people use facial prototypes when they
encounter different names.”
   Participants were given two faces and had to
determine which one was Tim and which one was
Bob. The researchers wrote that their participants
“overwhelmingly agreed” on which face belonged to
Tim and which face belonged to Bob.

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Hypotheses

   Alternative hypothesis: In the population, people
have a tendency to associate certain facial features
with a name. More specifically, the proportion of the
population that correctly matches the names with
the faces is greater than 0.5.
   Null hypothesis: In the population, people do not
have a tendency to associate certain facial features
with a name. More specifically, the proportion of the
population that correctly matches the names with
the faces is equal to 0.5.

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Did you get it correct?

Tim                               Bob

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Statistic---Simulate---Strength of Evidence

   Statistic: A recent class of statistics students
(our sample) replicated this study and 23 of
the 33 students (0.70) correctly identified the
face that belonged to Tim.
   Simulate: To simulate the null hypothesis,
we flip a coin 33 times and count the number
of heads each time. (Repeat this 1000 times)
   Strength of Evidence: Just 17 out of 1000
repetitions gave a result of 23 or more heads.
Quite unlikely if the null was true.
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1000 repetitions of flipping a fair coin 33
times and counting the number of heads

P-value = 0.017

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Conclusion

   We have evidence supporting that in the
population of interest, the proportion of
people that correctly identify which face
belongs to Tim and which belongs to Bob is
greater than 0.50.
   Thus based on our study we have evidence
to support people have a tendency to
associated certain facial features to a name.

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Assessment

   The Comprehensive Assessment of Outcomes in
Statistics (CAOS)
   Students in our randomization course took this pre-
and post-test in the Fall of 2009 (n = 202). These
results were compared with students that took our
traditional course in the Fall of 2007 (n = 198) and
those from a national representative sample (n =
768).
   Overall, learning gains were significantly higher for
students that took the randomization course when
compared to either those that took the traditional
course at Hope or the national sample.

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Questions where the new curriculum
faired significantly better
   Understanding that low p-values are desirable in
research studies (Tests of significance)
   Understanding that no statistical significance
does not guarantee that there is no effect (Tests
of significance)
   Ability to recognize a correct interpretation of a
p-value (Tests of significance)
   Ability to recognize an incorrect interpretation of
a p-value. Specifically, probability that a
treatment is not effective. (Tests of significance)
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Questions where the new curriculum
faired significantly better
   Understanding of the purpose of randomization
in an experiment (Data collection and design)
   Understanding of how to simulate data to find
the probability of an observed value (Probability)

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Questions where the new curriculum
faired significantly worse
   Ability to correctly estimate and compare
standard deviations for different histograms.
(Descriptive statistics)

Background ● Content ● Pedagogy ● Example ● Assessment ● Future
Moving Forward

   We welcome anyone that would like to field
test the book.
www.math.hope.edu/aasi
   Email
   Todd: swansont@hope.edu
   Jill: vanderstoepj@hope.edu

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