Filtering on

CO2 Meter

Diffraction-grating glasses

IPhone app provides interactive sound spectrogram (Spectogram Pro). Use slide whistle, stringed instrument, whistle (interactive exploration high vs. low), difference in timbre between male & female voices. What differences does the spectrograph instrument show between these sounds? How do these differences map onto differences you can hear? What does the spectrograph show that you can't know just by listening? Do you believe that what the spectrograph shows that you can't hear is real? Why or why not?

iPhone app (Vernier Video Physics) shows quantitative analysis of slices of time after videotaping the movement of a tossed ball (falling and bouncing).

Students line up in “human histograms,” demonstrating statistical dispersion and systematic bias.

Small group discussions, clicker-questions, and exercises to have students identify the statistical versus the systematic uncertainties in a number of scenarios. 

Spinning cylinders: A challenging puzzle (involving spinning a piece of plastic tubing, with markings on it) is presented to the students. The experimental conditions end up giving experiential demonstration of the usefulness of scientific optimism.  

Online Exercise: Causality Lab   

Online Exercise: Try five practice rounds at Guess the Correlation. Then refresh and test your average error based on the next five rounds. http://guessthecorrelation.com/  

Online exercise: Causality Lab

Playing a sound with Morse code signal hidden in static. Demonstrate how our ear/brain is highly developed to find the signal.  

Students write down a short phrase that they proceed, by stages, to hide in more and more noise (random substituted letters). Show the concept of “signal-to-noise ratio” as away to quantify at what point they can no longer recognize the message (the signal).  

Play the game Telephone with loud music on, and with silence. In which does the message change the least? (i.e., in which case does the "noise" make the signal harder to keep track of?).

Visual version: Handwrite a sentence in pencil. One at a time, each student copies what they think it says, then adds three lines somewhere in the text that alter the letters. By the end, it should be almost impossible to read. Do this with a random sentence, and with a famous sentence (e.g. “We the People of the United States, in Order to form a more perfect Union, establish Justice, insure domestic Tranquility, provide for the common defence, promote the general Welfare, and secure the Blessings of Liberty to ourselves and our Posterity, do ordain and establish this Constitution for the United States of America”). The famous sentence should be easier to read because it is familiar; we can more easily recognize the pattern.  Contrast with a nonsense line with strange words, e.g. some lines by Lewis Carroll students are not likely to recognize.  Demonstrates that it’s harder to detect a pattern when it’s different from the patterns we’re most used to finding.

Professor leaves room and students write down two lists of 40 coin-toss results: “heads, tails, tails, heads...,” the first generated by students sequentially calling out “heads” or “tails,” trying to simulate random coin flips and the second by actually flipping coins. The professor returns, and has to guess which is random and which is simulated random.

Stock picking activity. Students guess whether each of six fictional stocks will rise or fall. The instructor picks if each stock will rise or fall by flipping a coin, and then asks the students if anyone got all six right. Typically, at least one student will, just by chance, even though it is clearly a matter of chance.

Students guess the answers to 10 binary questions, write credence levels for each one. See the answers, calculate your calibration.

Many examples given from recent science presentations.  

A arbitrary topic is chosen for small group discussion (e.g. “Does testing in the schools help or hurt education?”), but during the discussion the students have to state their credence level (by saying a number between 0 and 100%) after every statement that they make which could have a credence level associated with it.

Together with the whole class, the professor shows how to develop Fermi-problem estimates of a given quantity, e.g. the amount per year that Americans spend on gas for personal transportation.  

In small groups, students use Fermi estimates to re-think the first-order, second-order, etc. parsing of US government spending on education, incarceration, and social security (following up the final activity from Topic XIIV, Orders of Understanding). The students frequently reach completely different orderings than they did in the previous class—and come within ~20% of the actual amounts spent.   

Small group exercises and clicker questions to demonstrate these effects with the students. 

Summaries of three relatively recent surprising science results (e.g. super-luminal neutrinos, bacteria with arsenic in their DNA, water with memory, cold fusion) and their follow-up in the scientific community are distributed among the groups. Each group explains the summary they read to other groups, so all have thought about each example. The groups discuss and vote with clickers on whether each article falls into the category of pathological science, poorly-done science, etc.   

Students make a measurement which is somewhat tricky to perform with two-digit precision, and experimental conditions are set up to show that the part of the class that was “blinded” gets a more accurate result.  

There could be a variant of the above incorporating blind analysis into a more typical lab class, where there is an expectation about the results, half the class does the analysis blinded, half the class does the analysis unblinded.

Students answer typical wisdom-of-crowd estimate questions using their clickers—but they can update their estimates as they see the histogram with the other students’ guesses. Afterwards, it is shown that the accuracy of the class’ mean estimate actually got worse as they continued to update their estimate—showing (if it works) that wisdom of crowd works best if the inputs are independent.    

In the two classes of this week, we try out an approach to random-sample-representative decision-making, using a panel of experts to answer questions generated by small deliberative groups, each with a moderator.