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How to Handle Intervals in a Simulation-Based Curriculum? Robin Lock Burry Professor of Statistics St. Lawrence University 2015 Joint Statistics Meetings.

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Presentation on theme: "How to Handle Intervals in a Simulation-Based Curriculum? Robin Lock Burry Professor of Statistics St. Lawrence University 2015 Joint Statistics Meetings."— Presentation transcript:

1 How to Handle Intervals in a Simulation-Based Curriculum? Robin Lock Burry Professor of Statistics St. Lawrence University 2015 Joint Statistics Meetings – Seattle, WA August 2015

2 Simulation-Based Inference (SBI) Projects Lock 5 lock5stat.com Tintle, et al math.hope.edu/isi Catalst www.tc.umn.edu/~catalst Tabor/Franklin www.highschool.bfwpub.com Open Intro www.openintro.org

3 SBI Blog www.causeweb.org/sbi/ How should we teach about intervals when using simulation-based inference?

4 Assumptions 1. We agree with George Cobb (TISE 2007): 2. Statistical inference has two main components: “Randomization-based inference makes a direct connection between data production and the logic of inference that deserves to be at the core of every introductory course.” Estimation (confidence interval) Hypothesis test (p-value)

5 Assumptions 3. For a randomized experiment to compare two groups: Confidence interval via simulation?  ??? Hypothesis test via simulation?  Randomization (permutation) test 3. For a parameter based on a single sample:

6 CI: Potential Initial Approaches 1. Invert hypothesis tests CI =plausible parameter values that would not be rejected 3. Traditional formulas

7 Example: Proportion of Orange Reese’s Pieces Key question: How accurate is a proportion estimated from 150 Reese’s pieces?

8 Invert the Test Guess/check:   95% CI for p (, 0.562) Repeat for the lower tail or use symmetry

9 Invert the Test Pros: Reinforces ideas from hypothesis tests Makes connection with CI as “plausible” values for the parameter Cons: Tedious (especially with randomization tests)-even with technology Harder to make a direct connection with variability (SE) of the sample statistic Requires tests first How do we do a CI for a single mean?

10 Bootstrap Basic idea: Sample (with replacement) from the original sample Compute the statistic for each bootstrap sample Repeat 1000’s of times to get bootstrap distribution Estimate the SE of the statistic

11 Simulated Reese’s Population Sample from this “population”

12 Original Sample Bootstrap Sample ●●●●●● Bootstrap Statistic Sample Statistic Bootstrap Statistic ●●●●●● Bootstrap Distribution Many times We need technology!

13 StatKey http://lock5stat.com/statkey

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17 Same process used for different parameters Bootstrap Confidence Intervals

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19 Why does the bootstrap work?

20 Sampling Distribution Population µ BUT, in practice we don’t see the “tree” or all of the “seeds” – we only have ONE seed

21 Bootstrap Distribution Bootstrap “Population” What can we do with just one seed? Grow a NEW tree! µ Chris Wild: Use the bootstrap errors that we CAN see to estimate the sampling errors that we CAN’T see.

22 Transition to Traditional Formulas Use z* or t*

23 Bootstrap Cons: Requires software Tedious to demonstrate “by hand” Doesn’t always “work”

24 Want to Know More? What Teachers Should Know about the Bootstrap: Resampling in the Undergraduate Statistics Curriculum Tim Hesterberg http://arxiv.org/abs/1411.5279 Thanks for listening rlock@stlawu.edu

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