Introducing Inference with Simulation Methods; Implementation at Duke University Kari Lock Morgan Department of Statistical Science, Duke University

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Introducing Inference with Simulation Methods; Implementation at Duke University Kari Lock Morgan Department of Statistical Science, Duke University Joint Statistical Meetings, San Diego 7/31/12

Methods of Inference Simulation methods intrinsically connected to the concepts minimal background knowledge needed same procedure applies to all statistics no conditions to check Traditional Methods (normal and t-based) familiarity expected after intro stat Use simulation methods to introduce inference, and then teach the traditional methods as “short-cut formulas”

Topics Introduction to Data Collecting data Describing data Introduction to Inference Confidence intervals (bootstrap) Hypothesis tests (randomization) Normal and t-based methods Chi-square and ANOVA Randomization and theoretical approaches Regression

Sleep versus Caffeine Mednick, Cai, Kanady, and Drummond (2008). “Comparing the benefits of caffeine, naps and placebo on verbal, motor and perceptual memory,” Behavioral Brain Research, 193,

Traditional Inference 1. Which formula? 2. Calculate numbers and plug into formula 3. Plug into calculator 4. Which theoretical distribution? 5. df? 6. find p-value < p-value < 0.05

Simulation Inference How extreme would a sample difference of 3 be, if there were no difference between sleep and caffeine for word recall? Simulate many randomizations, assuming no difference See what proportion of simulated randomizations yield differences in means as extreme as the observed 3

Randomization Test p-value Proportion as extreme as observed statistic observed statistic Distribution of Statistic Assuming Null is True StatKey at

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 (assuming… ?) Sample repeatedly from this mock population, by sampling with replacement from the original sample What is the average human body temperature? Bootstrapping

Bootstrap CI SE = Distribution of Bootstrap Statistics StatKey at  2  (98.044, ) Middle 95% of bootstrap statistics

Normal and t-based inference after bootstrapping and randomization: Students have seen the normal distribution repeatedly – CLT easy! Same idea, just using formula for SE and comparing to theoretical distribution Can go quickly through this! Theoretical Approach

p-value t-statistic

Introduce new statistic -  2 or F Students know that these can be compared to either a randomization distribution or a theoretical distribution Students are comfortable using either method, and see the connection! Chi-Square and ANOVA

Chi-Square Statistic Randomization Distribution Chi-Square Distribution (3 df) p-value =  2 statistic = p-value = 0.356

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

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 %8%

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

Further Information Want more information on teaching with this approach? Questions?