## Data Analysis in a Natural Selection Simulation

I really like the HHMI Biointeractive activity “Battling Beetles”. I have used it, in some iteration (see below), for the last 6 years to model certain aspects of natural selection. There is an extension where you can explore genetic drift and Hardy-Weinberg equilibrium calculations, though I have never done that with my 9th graders. If you stop at that point, the lab is lacking a bit in quantitative analysis. Students calculate phenotypic frequencies, but there is so much more you can do.  I used the lab to introduce the idea of a null hypothesis and standard error to my students this year, and I may never go back!

We set up our lab notebooks with a title, purpose/objective statements, and a data table. I provided students with an initial hypothesis (the null hypothesis), and ask them to generate an alternate hypothesis to mine (alternative hypothesis). I didn’t initially use the terms ‘null’ and ‘alternative’ for the hypotheses because, honestly, it wouldn’t have an impact on their success, and those are vocabulary words we can visit after demonstrating the main focus of the lesson. When you’re 14, and you’re trying to remember information from 6 other classes, even simple jargon can bog things down.  I had students take a random sample of 10 “male beetles” of each shell color, we smashed them together according the HHMI procedure, and students reported the surviving frequencies to me.

Once I had the sample frequencies, I used a Google Sheet to find averages and standard error, and reported those to my students. Having earlier emphasized “good” science as falsifiable, tentative and fallible, we began to talk about “confidence” and “significance” in research. What really seemed to work was this analogy: if your parents give you a curfew of 10:30 and you get home at 10:31, were you home on time? It isn’t a perfect comparison, and it is definitely something I’ll regret when my daughter is a few years older, but that seemed to click for most students. 10:31 isn’t 10:30, but if we’re being honest with each other, there isn’t a real difference between the two. After all, most people would unconsciously round 10:31 down to 10:30 without thinking. We calculated the average frequency changed from 0.5 for blue M&M’s to 0.53, and orange conversely moved from 0.5 to 0.47. So I asked them again: Does blue have an advantage? Is our result significant?

Short story, no; we failed to reject the null hypothesis. Unless you are using a 70% confidence interval, our result is not significantly different based on 36 samples. But it was neat to see the interval shrink during the day. After each class period, we added a few more samples, and the standard error measurement moved from 0.05 to 0.03 to 0.02. It was a really powerful way to emphasize the importance of sample size in scientific endeavors.

Should the pattern (cross-cutting concept!) hold across 20 more samples, the intervals would no longer overlap, and we could start to see something interesting. So if anyone has a giant bag of M&M’s lying around and you want to contribute to our data set, copy this sheet, add your results, and share it back my way. Hope we can collaborate!

Email results, comments, questions to Drew Ising at aising@usd348.com or drewising@gmail.com

–Versions of Battling Beetles Lab I’ve Tried–

HHMI Original

My “Student Worksheet” Edit

Lab Notebook Intro. from 2017-18

Lab Notebook Data from 2017-18

## In My Classroom #11: Cell Signaling

Welcome to the KABT blog segment, “In My Classroom”. This is a segment that will post about every two weeks from a different member. In 250 words or less, share one thing that you are currently doing in your classroom. That’s it.  Here we go:

Cell Signaling.

How does something so awesomely complex get such an innocuous name? The science behind how our cells communicate within and between their cell membranes was something that either I had never been taught (or blocked from my memory… sorry Mr. Kessler), but when I first started teaching College and AP Biology, I had to quickly get myself up to speed on. The underlying principle (like is true with any complex biochemical reaction series it seems) is actually fairly simple.

A signal is received. The message is passed from one messenger to the next. Eventually the message is received and a response occurs.

We can read about, model, diagram, memorize, write about, ponder upon, and generally learn about cell signaling in a number of “traditional” ways. But how do you experiment with it? And how can it be open (or even guided) inquiry?

Here’s what we try: Tastebud Transduction Lab

We start by reading and annotating an article, “Matters of Taste” from The Scientist on how our tastebuds are able to differentiate between all the different flavors we take in on a daily basis. I really like the detail they go into without losing their audience. [I have an edited version for 9th graders if you’re interested].

After a discussion in class, and a “Guided Reading” to reinforce the information from class, we begin our test by generating a list of things we think correlate to taste bud density, but that might not be directly related. For example, are “supertasters” pickier eaters? Students then design and conduct an experiment that looks for relationships between taste bud density and their chosen dependent variable.

Since it is so difficult to actual observe and manipulate these taste signaling pathways, I like to use this lab as a lesson in statistics, correlations, and significance. Students use a graphing program (plot.ly— it is AWESOME) to make a plot, then we get to talk about what R² really means, how correlation doesn’t imply causation, standard curves and outliers, and generally why stats are useful tools in research but can mislead even very intelligent, careful scientists.

I’m out of words (actually way over), but if you want to know more, email me (andrewising@gmail.com), comment here, or tweet me (@Mr_Ising or @ksbioteachers). One day Michael Ralph and I will get around to creating a bunch of “stats for science class” resources, but if there is interest here, it might give us a little more motivation to start earlier. Good luck, Jessica Otradovec Popescu, because you’re on the clock!

## American Scientist: Pizza Lunch Podcast

American Scientist Pizza Lunches are informal lectures where scientists present new research to an audience of non-scientists.  The talks are hosted in Research Triangle Park, North Carolina, at the headquarters of Sigma Xi, the Scientific Research Society, the publisher of American Scientist magazine.  Originally intended to help inform local science communicators about current and controversial topics, the Pizza Lunch talks are now available to anyone online, and new talks are posted periodically during the academic year.

Download individual talks through following the links below (clicking on the image above will take you to the general Pizza Lunch Podcast website).

1. Whole Genome Analysis in the Clinic
James Evans, clinical researcher in genetics at the University of North Carolina
Evans urges us to support genomics medicine research but asks us to temper our enthusiasm until it becomes a proven tool. (April 20, 2010)
2. Genomic and Personalized Medicine
Geoffrey Ginsburg, director of the Center for Genomic Medicine, Duke University
Ginsburg presents advances and ongoing research in personalized medicine, from prescribing cancer drugs to predicting flu symptoms. (March 30, 2010)
3. Metapopulation Dynamics of Oyster Restoration in Pamlico Sound, NC
David Eggleston, director of the Center for Marine Science and Technology, North Carolina State University
Eggleston discusses the challenges of conserving and restoring North Carolina coastal ecosystems, particularly oyster reefs. (January 26, 2010)
4. An Empire Lacking Food: The Astonishing Existence of Life on the Deep Seafloor
Craig McClain, assistant director of science, National Evolutionary Synthesis Center

McClain explores how the meager availability of food on the deep seafloor shapes the ecology and evolution of the animals that live there. (December 15, 2009)
5. The Evolution of the Human Capacity for Killing at a Distance
Steven Churchill, professor of evolutionary anthropology, Duke University
Churchill presents his research on the evolutionary origins of projectile weaponry, and how weapon use changed interactions between humans and other species—including, perhaps, the Neandertals. (October 20, 2009)
6. Our Energy Future: Science and Technology Challenges for the 21st Century
Thomas Meyer, director, Solar Energy Research Center, University of North Carolina, Chapel Hill
Meyer discusses the status of the world’s energy supply. In particular, he presents the idea that the sun’s energy could be used to make fuels from water and carbon dioxide for heating, transportation and energy storage. (September 24, 2009)
7. Everything Is Dangerous: A Controversy
S. Stanley Young, director of bioinformatics, National Institute of Statistical Sciences
Young critiques statistical analysis by some epidemiologists, especially their multiple testing of data sets obtained from observational studies. (April 22, 2009)
8. From Cloning to Stem Cells: How Can Pigs Help Us Solve Problems in Human Medicine?
Jorge Piedrahita, professor of genomics, North Carolina State University College of Veterinary Medicine

Piedrahita describes his research with cloned swine and how their abnormal growth provides insight into human placental defects, the ways transgenic pigs may help grow human tissue and how pigs could help advance stem cell therapies. (March 25, 2009)

I discovered this site via a twitter link today and thought it might provide another means of bringing scientists, although virtually, into the classroom.  Otherwise, if you don’t have a subscription to the American Scientist, it is one of my favorite periodicals.