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Introducing Inference with Bootstrapping and Randomization Kari Lock Morgan Department of Statistical Science, Duke University with.

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Presentation on theme: "Introducing Inference with Bootstrapping and Randomization Kari Lock Morgan Department of Statistical Science, Duke University with."— Presentation transcript:

1 Introducing Inference with Bootstrapping and Randomization Kari Lock Morgan Department of Statistical Science, Duke University kari@stat.duke.edu with Robin Lock, Patti Frazer Lock, Eric Lock, Dennis Lock ECOTS 5/16/12

2 Hypothesis Testing: 1.Use a formula to calculate a test statistic 2.This follows a known distribution if the null hypothesis is true (under some conditions) 3.Use a table or software to find the area in the tail of this theoretical distribution Traditional Methods

3 Plugging numbers into formulas and relying on deep theory from mathematical statistics does little for conceptual understanding With a different formula for each situation, students can get mired in the details and fail to see the big picture

4 Hypothesis Testing: 1. Decide on a statistic of interest 2. Simulate randomizations, assuming the null hypothesis is true 3.Calculate the statistic of interest for each simulated randomization 4.Find the proportion of simulated statistics as extreme or more extreme than the observed statistic Simulation Approach

5 Simulation Methods Intrinsically connected to concepts Same procedure applies to all statistics No conditions to check Minimal background knowledge needed

6 Simulation and Traditional? Simulation methods good for motivating conceptual understanding of inference However, familiarity with traditional methods (t-test) is still expected after intro stat Use simulation methods to introduce inference, and then teach the traditional methods as “short-cut formulas”

7 Topics Introduction to Data Collecting data Describing data Introduction to Inference Confidence intervals (bootstrap) Hypothesis tests (randomization) Normal and t-based methods Normal distribution Inference for means and proportions ANOVA, Chi-Square, Regression

8 Mind-set Matters In 2007, Dr. Ellen Langer tested her hypothesis that “mind-set matters” She recruited 84 hotel maids and randomly assigned half to a treatment and half to control The “treatment” was informing them that their work satisfies recommendations for an active lifestyle After 8 weeks, the informed group had lost 1.59 more pounds, on average, than the control group Is this difference statistically significant? Crum, A.J. and Langer, E.J. (2007). “Mind-Set Matters: Exercise and the Placebo Effect,” Psychological Science, 18:165-171.

9 Randomization Test on StatKey www.lock5stat.com/statkey 1.Test for difference in means 2.Choose “Weight Change vs Informed” from “Custom Dataset” drop down menu (upper right) 3.Generate randomization samples by clicking “Generate 1000 Samples” a few times 4. Click the box next to “Right tail” to pull up the proportion in the right tail 5.Edit the end point to match the observed statistic by clicking on the blue box on the x-axis

10 t-distribution

11 StatKey The probability of getting results as extreme or more extreme than those observed if the null hypothesis is true, is about.006. p-value Proportion as extreme as observed statistic observed statistic Distribution of Statistic Assuming Null is True

12 The p-value is the probability of getting a statistic as extreme (or more extreme) than the observed statistic, just by random chance, if the null hypothesis is true. Which part do students find most confusing? a)probability b)statistic as extreme (or more extreme) than the observed statistic c)just by random chance d)if the null hypothesis is true p-value

13 From just one sample, we’d like to assess the variability of sample statistics Imagine the population is many, many copies of the original sample (what do you have to assume?) Sample repeatedly from this mock population This is done by sampling with replacement from the original sample Bootstrapping

14 Bootstrap Confidence Interval Are you convinced? What proportion of statistics professors who watch this talk are planning on using simulation to introduce inference? Let’s use you as our sample, and then bootstrap to create a confidence interval! Are you planning on using simulation to introduce inference? a)Yes b)No www.lock5stat.com/statkey

15 Bootstrap CI on StatKey www.lock5stat.com/statkey 1.Confidence interval for single proportion 2.Click “Edit Data” and enter the data 3.Generate many bootstrap samples by clicking “Generate 1000 Samples” a few times 4. Click the box next to “Two-tail” 5.Edit the blue 0.95 in the middle to the desired level of confidence 6.Find the corresponding CI bounds on the x-axis

16 Student Preferences Which way did you prefer to learn inference (confidence intervals and hypothesis tests)? Bootstrapping and Randomization Formulas and Theoretical Distributions 10560 64%36% SimulationTraditional AP Stat3136 No AP Stat7424

17 Student Behavior Students were given data on the second midterm and asked to compute a confidence interval for the mean How they created the interval: Bootstrappingt.test in RFormula 9499 84%8%

18 A Student Comment " I took AP Stat in high school and I got a 5. It was mainly all equations, and I had no idea of the theory behind any of what I was doing. Statkey and bootstrapping really made me understand the concepts I was learning, as opposed to just being able to just spit them out on an exam.” - one of my students

19 Further Information Want more information on teaching with this approach? www.lock5stat.com Questions? kari@stat.duke.edu

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