Teaching Statistical Concepts with Activities, Data, and Technology Beth L. Chance and Allan J. Rossman Dept of Statistics, Cal Poly – San Luis Obispo.

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Teaching Statistical Concepts with Activities, Data, and Technology Beth L. Chance and Allan J. Rossman Dept of Statistics, Cal Poly – San Luis Obispo

Goals Acquaint you with recent recommendations and ideas for teaching introductory statistics  Including some very “modern” approaches  On top of some issues we consider essential Provide specific examples and activities that you might plug into your courses Point you toward online and print resources that might be helpful APSA Conference, Sept 20102

Schedule Introductions Opening Activity Activity Sessions  Data Collection  Data Analysis >  Randomness  Statistical Inference Resources and Assessment Q&A, Wrap-Up APSA Conference, Sept 20103

Requests Participate in activities  23 of them! We’ll skip/highlight some  Play role of student Good student, not disruptive student! Feel free to interject comments, questions APSA Conference, Sept 20104

GAISE Emphasize statistical literacy and develop statistical thinking Use real data Stress conceptual understanding rather than mere knowledge of procedures Foster active learning in the classroom Use technology for developing conceptual understanding and analyzing data Use assessments to improve and evaluate student learning www.amstat.org/education/gaise APSA Conference, Sept 20105

Opening Activity Naughty or nice? (Nature, 2007) Videos: http://www.yale.edu/infantlab/socialevaluation/ Helper-Hinderer.html http://www.yale.edu/infantlab/socialevaluation/ Helper-Hinderer.html Flip 16 coins, one for each infant, to decide which toy you want to play with (heads=helper) Coin Tossing Applet: http://www.rossmanchance.com/applets http://www.rossmanchance.com/applets APSA Conference, Sept 20106

3S Strategy Statistic Simulate  “Could have been” distribution of data for each repetition (under null model)  “What if” distribution of statistics across repetitions (under null model) Strength of evidence  Reject vs. plausible APSA Conference, Sept 20107

Summary Use real data/scientific studies  Emphasize the process of statistical investigation Stress conceptual understanding  Idea of p-value on day 1/in one day! Foster active learning  You are a dot on the board Use technology  Could this have happened “by chance alone”?  What if only 10 infants had picked the helper? APSA Conference, Sept 20108

Data Collection Activities: Activity 2: Sampling Words Circle 10 representative words in the passage Record the number of letters in each word Calculate the mean number of letters in your sample Dotplot of results… APSA Conference, Sept 20109

Sampling Words The population mean of all 268 words is 4.295 letters How many sample means were too high? Why do you think so many sample means are too high? APSA Conference, Sept 201010

Sampling Words “Tactile” simulation  Ask students to use computer or random number table to take simple random samples  Determine the sample mean in each sample  Compare the distributions APSA Conference, Sept 201011

Sampling Words Java applet  www.rossmanchance.com/applets/  Select “Sampling words” applet  Select individual sample of 5 words  Repeat  Select 98 more samples of size 5  Explore the effect of sample size  Explore the effect of population size APSA Conference, Sept 201012

Morals: Selecting a Sample Random Sampling eliminates human selection bias so the sample will be fair and unbiased/representative of the population. While increasing the sample size improves precision, this does not decrease bias. APSA Conference, Sept 201013

Activity 3: Night Lights and Near-Sightedness Quinn, Shin, Maguire, and Stone (1999) 479 children Did your child use a night light (or room light or neither) before age 2? Eyesight: Hyperopia (far-sighted), emmetropia (normal) or myopia (near- sighted)? APSA Conference, Sept 201014

Night Lights and Near-Sightedness DarknessNight lightRoom light Near- sighted 187841 Normal refraction 11411522 Far-sighted403912 APSA Conference, Sept 201015

Night Lights and Near-Sightedness APSA Conference, Sept 201016

Morals: Confounding Students can tell you that association is not the same as causation! Need practice clearly describing how confounding variable  Is linked to both explanatory and response variables  Provides an alternative explanation for observed association APSA Conference, Sept 201017

Activity 4: Have a Nice Trip Can instruction in a recovery strategy improve an older person’s ability to recover from a loss of balance?loss of balance  12 subjects have agreed to participate in the study  Assign 6 people to use the lowering strategy and 6 people to use the elevating strategyelevating What does “random assignment” gain you? APSA Conference, Sept 201018

Have a Nice Trip Randomizing subjects applet  How do the two groups compare? APSA Conference, Sept 201019

Morals Goal of random assignment is to be willing to consider the treatment groups equivalent prior to the imposition of the treatment(s). This allows us to eliminate all potential confounding variables as a plausible explanation for any significant differences in the response variable after the treatments are imposed. APSA Conference, Sept 201020

Activity 5: Cursive Writing Does using cursive writing cause students to score better on the SAT essay? APSA Conference, Sept 201021

Morals: Scope of Conclusions Allocation of units to groups By random assignmentNo random assignment Selection of units Random sampling A random sample is selected from one population; units are then randomly assigned to different treatment groups Random samples are selected from existing distinct populations Inferences to populations can be drawn Not random sampling A groups of study units is found; units are then randomly assigned to treatment groups Collections of available units from distinct groups are examined Cause and effect conclusions can be drawn The Statistical Sleuth, Ramsey and Schafer APSA Conference, Sept 201022

Activity 6: Memorizing Letters You will be asked to memorize as many letters as you can in 20 seconds, in order, from a sequence of 30 letters  Variables?  Type of study?  Comparison?  Random assignment?  Blindness?  Random sampling? More to come … APSA Conference, Sept 201023

Morals: Data Collection Quick, simple experimental data collection  Highlighting critical aspects of effective study design Can return to the data several times in the course APSA Conference, Sept 201024

Data Analysis Activities Activity 7: Matching Variables to Graphs Which dotplot belongs to which variable? Justify your answer APSA Conference, Sept 201025

Morals: Graph-sense Learn to justify opinions  Consistency, completeness Appreciate variability  Be able to find and explain patterns in the data APSA Conference, Sept 201026

Activity 8: Rower Weights 2008 Men’s Olympic Rowing Team APSA Conference, Sept 201027

Rower Weights APSA Conference, Sept 201028 MeanMedian Full Data Set197.96205.00 Without Coxswain201.17207.00 Without Coxswain or209.65209.00 lightweight rowers With heaviest at 249210.65209.00 With heaviest at 429219.70209.00 Resistance....

Morals: Rower Weights Think about the context “Data are numbers with a context”-Moore Know what your numerical summary is measuring Investigate causes for unusual observations  Anticipate shape APSA Conference, Sept 201029

Activity 9: Cancer Pamphlets Researchers in Philadelphia investigated whether pamphlets containing information for cancer patients are written at a level that the cancer patients can comprehend APSA Conference, Sept 201030

Cancer Pamphlets APSA Conference, Sept 201031

Morals: Importance of Graphs Look at the data Think about the question Numerical summaries don’t tell the whole story  “median isn’t the message” - Gould APSA Conference, Sept 201032

Activity 10: Draft Lottery Draft numbers (1-366) were assigned to birthdates in the 1970 draft lottery Find your draft number  Any 225s? APSA Conference, Sept 201033

Draft Lottery APSA Conference, Sept 201034

monthmedian January 211.0 February 210.0 March 256.0 April 225.0 May 226.0 June 207.5 month median July 188.0 August 145.0 September 168.0 October 201.0 November 131.5 December 100.0 Draft Lottery APSA Conference, Sept 201035

Draft Lottery APSA Conference, Sept 201036

Morals: Statistics matters! Summaries can illuminate Randomization can be difficult APSA Conference, Sept 201037

Activity 11: Televisions and Life Expectancy Is there an association between the two variables? So sending televisions to countries with lower life expectancies would cause their inhabitants to live longer? r =.743 APSA Conference, Sept 201038

Morals: Confounding Don’t jump to conclusions from observational studies The association is real but consider carefully the interpretation of graph and wording of conclusions (and headlines) APSA Conference, Sept 201039

Activity 6 Revisited (Memorizing Letters) Produce, interpret graphical displays to compare performance of two groups  Does research hypothesis appear to be supported?  Any unusual features in distributions? APSA Conference, Sept 201040

Lunch! Questions?  Write down and submit any questions you have thus far on the statistical or pedagogical content… APSA Conference, Sept 201041

Exploring Randomness Activity 12: Random Babies Last NamesFirst Names JonesJerry MillerMarvin SmithSam WilliamsWilly APSA Conference, Sept 201042

Random Babies Last NamesFirst Names JonesMarvin Miller Smith Williams APSA Conference, Sept 201043

Random Babies Last NamesFirst Names JonesMarvin MillerWilly Smith Williams APSA Conference, Sept 201044

Random Babies Last NamesFirst Names JonesMarvin MillerWilly SmithSam Williams APSA Conference, Sept 201045

Last NamesFirst Names JonesMarvin MillerWilly SmithSam WilliamsJerry Random Babies APSA Conference, Sept 201046

Last NamesFirst Names JonesMarvin MillerWilly SmithSam 1 match WilliamsJerry Random Babies APSA Conference, Sept 201047

Random Babies Long-run relative frequency  Applet: www.rossmanchance.com/applets/  “Random Babies” APSA Conference, Sept 201048

Random Babies: Mathematical Analysis 1234 1243 1324 1342 1423 1432 2134 2143 23142341 2413 2431 3124 3142 32143241 3412 3421 4123 4132 42134231 4312 4321 APSA Conference, Sept 201049

Random Babies 1234 1243 1324 1342 1423 1432 4 22 1 1 2 2134 2143 23142341 2413 2431 2 0 1 0 0 1 3124 3142 32143241 3412 3421 1 0 2 1 0 0 4123 4132 42134231 4312 4321 0 1 1 2 0 0 APSA Conference, Sept 201050

0 matches: 9/24=3/8 1 match: 8/24=1/3 2 matches: 6/24=1/4 3 matches: 0 4 matches: 1/24 Random Babies APSA Conference, Sept 201051

Goal: Interpretation in terms of long-run relative frequency, average value  30% chance of rain… First simulate, then do theoretical analysis  Able to list sample space  Short cuts when are actually equally likely Simple, fun applications of basic probability Morals: Treatment of Probability APSA Conference, Sept 201052

ELISA test used to screen blood for the AIDS virus  Sensitivity: P(+|AIDS)=.977  Specificity: P(-|no AIDS)=.926  Base rate: P(AIDS)=.005 Find P(AIDS|+)  Initial guess?  Bayes’ theorem?  Construct a two-way table for hypothetical population Activity 13: AIDS Testing APSA Conference, Sept 201053

Positive Negative Total AIDS No AIDS Total 1,000,000 AIDS Testing APSA Conference, Sept 201054

Positive Negative Total AIDS 5,000 No AIDS 995,000 Total 1,000,000 AIDS Testing APSA Conference, Sept 201055

Positive Negative Total AIDS4885115 5,000 No AIDS 995,000 Total 1,000,000 AIDS Testing APSA Conference, Sept 201056

Positive Negative Total AIDS4885115 5,000 No AIDS73,630921,370 995,000 Total 1,000,000 AIDS Testing APSA Conference, Sept 201057

Positive Negative Total AIDS4885115 5,000 No AIDS73,630921,370 995,000 Total78,515921,485 1,000,000 AIDS Testing APSA Conference, Sept 201058

Positive Negative Total AIDS4885115 5,000 No AIDS73,630921,370 995,000 Total78,515921,485 1,000,000 P(AIDS|+) = 4885/78,515=.062 AIDS Testing APSA Conference, Sept 201059

Positive Negative Total AIDS4885115 5,000 No AIDS73,630921,370 995,000 Total78,515921,485 1,000,000 P(AIDS|+) = 4885/78,515=.062 P(No AIDS|-) = 921,370/921,485 =.999875 AIDS Testing APSA Conference, Sept 201060

Intuition about conditional probability can be very faulty  Confront misconception head-on Conditional probability can be explored through two-way tables  Treatment of formal probability can be minimized Morals: Surprise Students! APSA Conference, Sept 201061

Activity 14: Reese’s Pieces APSA Conference, Sept 201062

Take sample of 25 candies Sort by color Calculate the proportion of orange candies in your sample Construct a dotplot of the distribution of sample proportions Reese’s Pieces APSA Conference, Sept 201063

Turn over to technology  Reeses Pieces applet Reeses Pieces applet (www.rossmanchance.com/applets/) Reese’s Pieces APSA Conference, Sept 201064

Study randomness to develop intuition for statistical ideas  Not probability for its own sake Always precede technology simulations with physical ones Apply more than derive formulas Morals: Sampling Distributions APSA Conference, Sept 201065

Left FrontRight Front Left RearRight Rear Activity 15: Which Tire? APSA Conference, Sept 201066

People tend to pick “right front” more than ¼ of the time Variable = which tire pick  Categorical (binary) How often would we get data like this by chance alone?  Determine the probability of obtaining at least as many “successes” as we did if there were nothing special about this particular tire. Which Tire? APSA Conference, Sept 201067

Let  = proportion of all … who pick right front H 0 :  =.25 H a :  >.25 Test statistic z = p-value = Pr(Z>z)  How does this depend on n?  Test of Significance Calculator Which Tire? APSA Conference, Sept 201068

nz-statisticp-value 501.14.127 1001.62.053 1501.98.024 4003.23.001 10005.11.000… Which Tire? APSA Conference, Sept 201069

Fun simple data collection Effect of sample size  hard to establish result with small samples Never “accept” null hypothesis Morals: Formal Statistical Inference APSA Conference, Sept 201070

Activity 16: Kissing the Right Way Biopsychology observational study  Güntürkün (2003) recorded the direction turned by kissing couples to see if there was also a right- sided dominance. APSA Conference, Sept 201071

Kissing the Right Way Is 1/2 a plausible value for  the probability a kissing couple turns right? Coin Tossing applet Is 2/3 a plausible value for  the probability a kissing couple turns right?  Is the observed result in the tail of the “what if” distribution? APSA Conference, Sept 201072

Kissing the Right Way Determine the plausible values for  the probability a kissing couple turns right… The values that produce an approximate p- value greater than.05 are not rejected and are therefore considered plausible values of the parameter. The interval of plausible values is sometimes called a confidence interval for the parameter. APSA Conference, Sept 201073

Kissing the Right Way How does this compare to estimate + margin of error? Or the even simpler approximation? APSA Conference, Sept 201074

Morals: Kissing the Right Way Interval estimation as (more?) important as significance Confidence interval as set of plausible (not rejected) values Interpretation of margin-of-error APSA Conference, Sept 201075

Activity 17: Reese’s Pieces Revisited Calculate 95% confidence interval for  from your sample proportion of orange  Does everyone have same interval?  Does every interval necessarily capture  ?  What proportion of class intervals would you expect? Simulating Confidence Intervals applet  What percentage of intervals succeed?  Change confidence level, sample size APSA Conference, Sept 201076

Morals: Reese’s Pieces Revisited Interpretation of confidence level  In terms of long-run results from taking many samples Effects of confidence level, sample size on confidence interval APSA Conference, Sept 201077

78 Example 18: Dolphin Therapy Subjects who suffer from mild to moderate depression were flown to Honduras, randomly assigned to a treatment APSA Conference, Sept 201078

Dolphin Therapy Is dolphin therapy more effective than control? Core question of inference:  Is such an extreme difference unlikely to occur by chance (random assignment) alone (if there were no treatment effect)? APSA Conference, Sept 201079

80 Some approaches Could calculate test statistic, p-value from approximate sampling distribution (z, chi-square)  But it’s approximate  But conditions might not hold  But how does this relate to what “significance” means? Could conduct Fisher’s Exact Test  But there’s a lot of mathematical start-up required  But that’s still not closely tied to what “significance” means Even though this is a randomization test APSA Conference, Sept 201080

81 3S Approach Simulate random assignment process many times, see how often such an extreme result occurs  Assume no treatment effect (null model)  Re-randomize 30 subjects to two groups (using cards) Assuming 13 improvers, 17 non-improvers regardless  Determine number of improvers in dolphin group Or, equivalently, difference in improvement proportions  Repeat large number of times (turn to computer)  Ask whether observed result is in tail of what if distribution Indicating saw a surprising result under null model Providing evidence that dolphin therapy is more effective APSA Conference, Sept 201081

82 Analysis http://www.rossmanchance.com/applets/ Dolphin Study applet APSA Conference, Sept 201082

83 Conclusion Experimental result is statistically significant  And what is the logic behind that? Observed result very unlikely to occur by chance (random assignment) alone (if dolphin therapy was not effective) APSA Conference, Sept 201083

Morals Re-emphasize meaning of significance and p-value  Use of randomness in study Focus on statistical process, scope of conclusions APSA Conference, Sept 201084

85 Activity 19: Sleep Deprivation Does sleep deprivation have harmful effects on cognitive functioning three days later?  21 subjects; random assignment Core question of inference:  Is such an extreme difference unlikely to occur by chance (random assignment) alone (if there were no treatment effect)? APSA Conference, Sept 201085

86 Sleep Deprivation Simulate randomization process many times under null model, see how often such an extreme result (difference in group medians or means) occurs Start with tactile simulation using index cards  Write each “score” on a card  Shuffle the cards  Randomly deal out 11 for deprived group, 10 for unrestricted group  Calculate difference in group medians (or means)  Repeat many times (Randomization Tests applet) APSA Conference, Sept 201086

Sleep Deprivation Conclusion: Fairly strong evidence that sleep deprivation produces lower improvements, on average, even three days later  Justification: Experimental results as extreme as those in the actual study would be quite unlikely to occur by chance alone, if there were no effect of the sleep deprivation APSA Conference, Sept 201087

Exact randomization distribution Exact p-value 2533/352716 =.0072 (for difference in means) APSA Conference, Sept 201088

Morals: Randomizations Tests Emphasizes core logic of inference  Takes advantage of modern computing power  Easy to generalize to other statistics APSA Conference, Sept 201089

Activity 6 Revisited (Memorizing Letters) Conduct randomization test to assess strength of evidence in support of research hypothesis  Enter data into applet Summarize conclusion and reasoning process behind it Does non-significant result indicate that grouping of letters has no effect? APSA Conference, Sept 201090

Activity 20: Cat Households 47,000 American households (2007) 32.4% owned a pet cat  or the other way around! test statistic: z=-4.29 p-value: virtually zero 99% CI for  (.31844,.32956) APSA Conference, Sept 201091

Morals: Limits of statistical significance Statistical significance is not practical significance  Especially with large sample sizes Accompany significant tests with confidence intervals whenever possible APSA Conference, Sept 201092

Activity 21: Female Senators 17 women, 83 men in 2010 95% CI for  : =.170 +.074 = (.096,.244) APSA Conference, Sept 201093

Always consider sampling procedure  Randomness is key assumption  Garbage in, garbage out Inference is not always appropriate!  Sample = population here Morals: Limitations of Inference APSA Conference, Sept 201094

Activity 22: Game Show Prices Sample of 208 prizes from The Price is Right Examine a histogram 99% confidence interval for the mean Technical conditions? What percentage of the prizes fall in this interval? Why is this not close to 99%? APSA Conference, Sept 201095

Morals: Cautions/Limitations Prediction intervals vs. confidence intervals Constant attention to what the “it” is APSA Conference, Sept 201096

Activity 23: Government Spending 2004 General Social Survey: Is there an association between American adults’ opinion on federal government spending on the environment and political inclinations? APSA Conference, Sept 201097

Government Spending Descriptive analysis APSA Conference, Sept 201098 LiberalModerateConservativeTotal Too Much1173250 About Right278091198 Too Little127158113398 Total155255236646

Government Spending Inferential analysis – 3S approach 1. Chi-square statistic 2. Simulate sampling distribution of chi-square test statistic under null hypothesis of no association Randomly mix up political inclinations, determine “could have been” table Repeat many times and examine “what if” distribution of chi-square values under null hypothesis APSA Conference, Sept 201099

Government Spending 3. Strength of evidence  Is observed chi-square value in tail of distribution? Summarize: What conclusions should be drawn?  Very statistically significant  Not cause and effect  Ok to generalize to adult Americans APSA Conference, Sept 2010100

Government Spending What about federal spending on the space program? APSA Conference, Sept 2010101 More or less evidence of association? Larger or smaller p-value?

General Advice Emphasize the process of statistical investigations, from posing questions to collecting data to analyzing data to drawing inferences to communicating findings Use simulation, both tactile and technology-based, to explore concepts of inference and randomness Draw connections between how data are collected (e.g., random assignment, random sampling) and scope of conclusions to be drawn (e.g., causation, generalizability) Use real data from genuine studies, as well as data collected on students themselves Present important studies (e.g., draft lottery) and frivolous ones (e.g., flat tires) and especially studies of issues that are directly relevant to students (e.g., sleep deprivation) APSA Conference, Sept 2010102

General Advice (cont.) Lead students to “discover” and tell you important principles (e.g., association does not imply causation) Keep in mind the research question when analyzing data Graphical displays can be very useful Summary statistics (measures of center and spread) are helpful but don’t tell whole story; consider entire distribution Develop graph-sense, number-sense by always thinking about context Use technology to reduce the burden of rote calculations, both for analyzing data and exploring concepts Emphasize cautions and limitations with regard to inference procedures APSA Conference, Sept 2010103

Implementation Suggestions Take control of the course Collect data from students Encourage predictions from students Allow students to discover/tell you findings Precede technology simulations with tactile Promote collaborative learning Provide lots of feedback Follow activities with related assessments Intermix lectures with activities Don’t underestimate ability of activities to teach materials Have fun! APSA Conference, Sept 2010104

Suggestion #1 Take control of the course  Not “control” in usual sense of standing at front dispensing information  But still need to establish structure, inspire confidence that activities, self-discovery will work  Be pro-active in approaching students Don’t wait for students to ask questions of you Ask them to defend their answers Be encouraging  Instructor as facilitator/manager APSA Conference, Sept 2010105

Suggestion #2 Collect data from students  Leads them to personally identify with data, analysis; gives them ownership  Collect anonymously  Can do out-of-class  E.g., matching variables to graphs APSA Conference, Sept 2010106

Suggestion #3 Encourage predictions from students  Fine (better…) to guess wrong, but important to take stake in some position  Directly confront common misconceptions Have to “convince” them they are wrong (e.g., Gettysburg address) before they will change their way of thinking  E.g., AIDS Testing APSA Conference, Sept 2010107

Suggestion #4 Allow students to discover, tell you findings  E.g., Televisions and life expectancy “I hear, I forget. I see, I remember. I do, I understand.” -- Chinese proverb APSA Conference, Sept 2010108

Suggestion #5 Precede technology simulations with tactile/ concrete/hands-on simulations  Enables students to understand process being simulated  Prevents technology from coming across as mysterious “black box”  E.g., Gettysburg Address (actual before applet) APSA Conference, Sept 2010109

Suggestion #6 Promote collaborative learning  Students can learn from each other  Better yet from “arguing” with each other  Students bring different background knowledge E.g., Matching variables to graphs APSA Conference, Sept 2010110

Suggestion #7 Provide lots of feedback  Danger of “discovering” wrong things  Provide access to “model” answers after the fact Could write “answers” on board Could lead discussion/debriefing afterward APSA Conference, Sept 2010111

Suggestion #8 Follow activities with related assessments  Or could be perceived as “fun and games” only Require summary paragraphs in their own words Clarify early (e.g., quizzes) that they will be responsible for the knowledge  Assessments encourage students to grasp concept Can also help them to understand concept  E.g., fill in the blank p-value interpretation APSA Conference, Sept 2010112

Suggestion #9 Inter-mix lectures with activities  One approach: Lecture on a topic after students have performed activity Students better able to process, learn from lecture having grappled with issues themselves first  Another approach: Engage in activities toward end of class period Often hard to re-capture students’ attention afterward  Need frequent variety APSA Conference, Sept 2010113

Suggestion #10 Do not under-estimate ability of activities to “teach” material  No dichotomy between “content” and “activities”  Some activities address many ideas E.g. “Gettysburg Address” activity  Population vs. sample, parameter vs. statistic  Bias, variability, precision  Random sampling, effect of sample/population size  Sampling variability, sampling distribution, Central Limit Theorem (consequences and applicability) APSA Conference, Sept 2010114

Suggestion #11 Have fun! APSA Conference, Sept 2010115

Assessment Advice Two sample final exams  Carefully match the course goals  Be cognizant of any review materials you have given the students  Use real data and genuine studies  Provide students with guidance for how long they should spend per problem  Use multiple parts to one context but aim for independent parts (if a student cannot answer part (a) they may still be able to answer part (b))  Use open-ended questions requiring written explanation  Aim for at least 50% conceptual questions rather than pure calculation questions  (Occasionally) Expect students to think, integrate, apply beyond what they have learned. Sample guidelines for student projects APSA Conference, Sept 2010116

Promoting Student Progress Document and enhance student learning Element of instruction Interactive feedback loop  Diagnostic with indicators for change  Throughout the course  To student and instructor  Encourage self-evaluation Multiple indicators APSA Conference, Sept 2010117

Student Projects Best way to demonstrate to students the practice of statistics Experience the fine points of research Experience the “messiness” of data From beginning to end  Formulation and Explanation  Constant Reference statweb.calpoly.edu/bchance/stat217/projects.html APSA Conference, Sept 2010118

Resources www.causeweb.org APSA Conference, Sept 2010119

Resources GAISE reports APSA Conference, Sept 2010120

Resources TeachingWithData.org APSA Conference, Sept 2010121

Resources Inter-University Consortium for Political and Social Research (ICPSR) APSA Conference, Sept 2010122

Resources www.rossmanchance.com/applets/ http://statweb.calpoly.edu/csi/ APSA Conference, Sept 2010123

Resources https://app.gen.umn.edu/artist/ APSA Conference, Sept 2010124

Resources http://lib.stat.cmu.edu/DASL/ www.amstat.org/publications/jse/ /jse_data_archive.html APSA Conference, Sept 2010125

Background Readings Guidelines for teaching introductory statistics Reflections on what distinguishes statistical content and statistical thinking Educational research findings and suggestions related to teaching statistics Collections of resources and ideas for teaching statistics Suggestions and resources for assessing student learning in statistics APSA Conference, Sept 2010126

Thanks very much! Questions, comments? bchance@calpoly.edu arossman@calpoly.edu APSA Conference, Sept 2010127

My Syllabus Briefly W1: Collecting Data W2: Graphical/Numerical W3: NormalProject 1 W4: Exam 1 Project 2 W5: Probability W6: Sampling Distributions W7: Inference W8: Inference APSA Conference, Sept 2010128

My Syllabus Briefly W9: Two Samples W10: Exam IIProject 3 W11: Two variables W12: Inference for Regression W13: Two-way TablesProject 4 W14: ANOVA W15: Presentations APSA Conference, Sept 2010129

Non-simulation approach Exact randomization distribution  Hypergeometric distribution  Fisher’s Exact Test  p-value = =.0127 APSA Conference, Sept 2010130

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