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  • An activity to gather data on oranges for use in a unit on descriptive statistics.  The idea was presented at the 2019 USCOTS meeting by Katherine Frey Froslie and is described in her blog at https://www.statistrikk.no/2019/05/19/oranges-are-the-new-statistics/

    Here students measure how long it takes them to peel an orange (an easy to peel variety is recommended for in-class usage), what the orange weighs (possibly with and without the peel), and how many wedges are in the orange.  This creates a data set with both discrete (# of wedges) and continuous variables (time to peel, weight, percentage of orange weight in the peel) to be used for description. Other variables can be added through class discussion depending on student interest. An easy to peel variety of oranges  

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  • Summary: Through generating, collecting, displaying, and analyzing data, students are given the opportunity to explore a variety of descriptive statistical techniques and develop an understanding of the distinction between theoretical, subjective, and empirical (or experimental) probabilities. These concepts are developed with activities using Hershey KissesTM and may be extended to introduce the sampling distribution of a sample proportion. The activities are described in M. Richardson and S. Haller. (2002), “What is the Probability of a Kiss? (It's Not What You Think),” Journal of Statistics Education, 10(3), https://www.tandfonline.com/doi/full/10.1080/10691898.2002.11910683

    Specifics: The main activity uses Hershey’s Kisses to explore the concept of probability. Three specific sub-activities are performed such as: 

    1. Students explore the empirical probability that a plain Hershey’s Kiss will land on its flat base when spilled from a cup. 
    2. Students make predictions about the probability of an almond Hershey’s Kisses landing on its base when spilled from a cup, after having experimented with the plain Kisses.
    3. Students explore the properties of the distribution of a sample proportion to see whether the percentages of base landings have a specified distribution and whether they think that the number of Kisses tossed affects the shape or the mean and standard deviation of this distribution.

    (Resource photo illustration by Barbara Cohen, 2020; this summary compiled by Bibek Aryal)

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  • Summary: This High School AP activity examines whether students can tell the difference between CokeTM and PepsiTM by taste? During the “tasting part”, data are collected and the class keeps track of how many students can differentiate between Coke and Pepsi. During the “simulation part” of the activity, a simulation is conducted with dice. Finally, students compare their classroom results in the taste test with the simulated results about what would happen when subjects just guess randomly from the three possible choices. The activity is described in F. Bullard, “AP Statistics: Coke Versus Pepsi: An Introductory Activity for Test of Significance: AP Central – The College Board,” 2017 on the AP Central website at https://apcentral.collegeboard.org/courses/ap-statistics/classroom-resources/coke-versus-pepsi-introductory-test-significance

     

    Specifics: The activity is performed in the following steps:

    1. The Tasting part:
      1. First, two students will label three cup positions “A,” “B,” and “C.” Then they will roll a die and pour drinks into the cups such that all combinations of two of one drink and one of the other are represented, and the die roll makes each combination equally likely and keep track of the treatment.
      2. Students will be called out into the hall one by one to taste the three drinks and decide which cup contains the different drink. They do not need to identify the drinks as Coke or Pepsi, they only have to identify the cup containing the different soda, either A, B, or C.
    2. The Simulation part:
      1. The next stage of the discussion is to ask the students how many correct identifications they need before they can conclude that people were not just randomly guessing: “11 out of 30 is more than a third, but not enough more to be convincing, right?” Students will probably volunteer different dividing lines, but they will not be good at defending them. At the point when all the students understand the question but are unsure of how to answer it, the dice should be introduced into the activity.
      2. The students can suggest a simulation in which two die outcomes (say, 1 and 2) are considered a correct cup identification, and the other four die outcomes (say, 3, 4, 5, and 6) are considered incorrect cup identifications. Demonstrate by rolling a set of dice or one die many times. You should have as many die rolls as there are subjects in the study. Count the 1s and 2s. Suppose there are 8 out of 30 that “guessed correctly.” On your number line at the blackboard, make an X over the number 8. The students or group of students should do five or 10 simulations each (it’s good to have about 100—200 simulations) and then come to the blackboard and stack their Xs over the appropriate integers, making a histogram of the distribution of “number of correct cup identifications if everyone is randomly guessing.”
    3. Conclusion:
      1. Upon the conclusion of the tasting, the number of correct identifications is then counted. At this point, if the number is unusually high (say, 18 out of 30), then most students are prepared to conclude (correctly) that there is evidence that at least some people can tell the difference between Coke and Pepsi.
      2. Some statement like this would be great: “If everyone were randomly guessing, we would almost never see 18 students get it right by luck, because we did that 100 times with dice, and the highest we ever got was 16, and that was only once.”
      3. In the author’s experience, usually, about half or a little more will identify the correct drink. When the author, did this activity with a class: 13 out of 21 students correctly identified the different drinks.

    (Resource photo illustration by Barbara Cohen, 2020; this summary compiled by Bibek Aryal)

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  • Summary: A classroom activity using dried split peas exploring the reliability of a basic capture-mark-recapture method of population estimation is described using great whale conservation as a motivating example. The activity was described in C. du Feu, “Having a whale of a time,” Teaching Statistics, 31 (3) (2009), 66-71.

    Specifics: The hands-on activity uses dried split peas and involves much larger populations and has two advantages. Firstly, the split-pea populations are too large for any sensible student to contemplate counting the full population. Secondly, unlike SmartiesTM, or M&M’sTM dried split peas will not suffer loss through eating (so there is a fixed population size to be estimated). Beforehand, soak some split peas in colored food dye or simply buy both green peas and yellow peas. Students add exactly 50 of the differently colored peas to each population of unmarked split peas. We now have hundreds, if not thousands, of members in each population of which 50 were ‘captured’ and marked in the first sampling event. The sampling can be done using a teaspoon of about 5mL capacity, which gives samples of about 50 individuals. The number of marked and unmarked split peas in the spoon are counted and a population estimate is made. The peas are replaced, the populations is mixed (stirring or shaking with the lid on) and the next sample is taken. This is repeated as long as required. Once there are sufficient estimates, the sampling can be drawn to a close and discussion of the estimates can take place. 

    Supplementary materials include expository material on the motivating example and student worksheets.

    (Resource photo illustration by Barbara Cohen, 2020; this summary compiled by Bibek Aryal)

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  • A hands-on activity using the capture-recapture method to estimate the number of SmartiesTM candy pieces in a population and to study the variability in individual estimates compared to an estimate based on the mean of many estimates.  The activity was described in B. Dudley, "A practical study of the capture/recapture method of estimating population size, Teaching Statistics, 5 (3) (1983), 66-70.

    Summary: A hands-on activity to study the variability of the capture/recapture technique for estimating population sizes, demonstrated using a population of Smarties candy as an example. 

    Specifics: The capture/recapture technique is used to arrive at estimates of the size of population of mobile animals using the formula: 
    a/d = c/b, where
    a = number marked and released into the population,
    b = size of the second catch,
    c = the number recaptured in the second catch,
    d = the size of the population as a whole
    The contents of a box of smarties are poured into a saucer and all the sweets of red colour were counted (=a). After that, all the sweets are poured into a paper bag and shaken thoroughly. With an egg cup, without looking at the bag, the second sample (=b) was scooped and the number of red ones recaptured were recorded (=c). This exercise was repeated ten times and the mean was calculated. Finally, the number of Smarties in the model population were counted and compared with the estimates derived from the sampling. Students learn about the variability of individual estimates, which is quite large (remember that the mean of the estimate here is actually infinite since an observation of zero tagged items results in an infite estimate).

    (Resource photo illustration by Barbara Cohen, 2020; this summary compiled by Bibek Aryal)

     

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  • A hands-on activity using the capture-recapture method to estimate the number of M&M’sTM in a population The activity was described in G. D. Bisbee and D. M. Conway, “Studying proportions using the capture-recapture method”, Mathematics Teacher, 92 (3) (1999), 215-218.

    Summary: Scientists use the capture-recapture method as a tool to estimate population size. Animals are captured, tagged, and then released back into the population. Later, a sample is captured and a proportion used to estimate population size.

    Specifics: Let us say that we sample a beetle population of unknown size. We capture and mark ten of those beetles with a spot of India ink, then return them to the population and give them time to mix in with the population. We then recapture another sample consisting of eight beetles, one of which was previously marked. We substitute the numbers into the foregoing proportion to estimate the population size, getting 1/8 = 10/(Pop size). Solving for the Pop size gives us an estimated population of eighty beetles. Students are, predictably, less than enthusiastic about having to handle the creepy-crawly critters so this activity uses a population of M&M’s of unknown size to estimate. Each team of two to four students receives some M&M in a paper cup, which is covered on top with crumpled paper towels. The students “tag” the M&M’s from a random sample and then, after mixing them back in, sample again to estimate the number in the cup (they can later check how far off their estimates  were and compare to other teams).

    (Resource photo illustration by Barbara Cohen, 2020; this summary compiled by Bibek Aryal)

     

     

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  • Summary: This article describes the capture-recapture method of estimating the size of a population of fish in a pond and illustrates it with both a “hands-on” classroom activity using Pepperidge Farm GoldfiishTM crackers and a computer simulation that investigates two different estimators of the population size.  The activity was described in R. W. Johnson, “How many fish are in the pond?,”Teaching Statistics, 18 (1) (1996), 2-5

    https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-9639.1996.tb00882.x

    Specifics: To illustrate the capture-recapture method in the classroom, two different varieties of Pepperidge Farm GoldfishTM crackers are used. The instructor places all of the Goldfish from a full bag of the original variety in a bowl to correspond to the initial state of the pond (the instructor should have previously counted the true number from the bag, which turned out to be 323 in the paper’s example). Students then captured c = 50 of these fish and replaced them with 50 Goldfish of a flavored variety of different color. After mixing the contents of the bowl, t=6 ‘tagged’ fish - fish of the flavored variety were found in a recaptured sample size of r = 41, giving the estimate cr/t= 341. This used the maximum likelihood (ML method. To examine the behavior of the MLE the capture-recapture ML  method is repeated 1000 times using a computer simulation. The distribution of the results will be heavily skewed since the MLE is quite biased (in fact, since there is positive probability that t = 0, the MLE has an infinite expectation). The simulation is then redone using Seber’s biased-corrected estimate = [(c+1)(r+1)/(t+1)] – 1.  After the true value of the population size is revealed by the instructor, students see that the average of the 1000 new simulations show that the biased-corrected version is indeed closer to the truth (and also that the new estimate has less variability).

    (Resource photo illustration by Barbara Cohen, 2020; this summary compiled by Bibek Aryal)

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  • A cartoon suitable for use in teaching about scatterplots and correlation. The cartoon is number 388 (Feb, 2008) from the webcomic series at xkcd.com created by Randall Munroe. Free to use in the classroom and on course web sites under a creative commons attribution-non-commercial 2.5 license.

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  • A joke to use in presentations about the importance of control and replication in experimentation.  The joke was written by Larry Lesser (The University of Texas at El Paso) and Dennis Pearl (Penn State University) in March 2020.

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  • A cartoon to initiate discussions about how the correlation is a unitless number that does not change with changes in the units of the variables involved.  The cartoon was created in February 2020 by British caetoonist John Landers based on an idea by Dennis Pearl (Penn State) and Larry Lesser (Univ of Texas at El Paso). An outline of a lesson plan for the use of the cartoon is given in a 2020 Teaching Statistics article by Dennis Pearl and Larry Lesser.

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