IPS - Day 55

Class started today with students evaluating their posters. I first had them look at two posters that were created by previous classes. These posters were scored in the 85% to 92% range. After reviewing the posters, I asked students to score their on posters on a 20 point scale. I told them I wanted an honest evaluation of what they produced and how well they communicated statistical thinking, specifically with providing purpose, hypotheses, processes, analysis, and conclusions.

I will also score the posters and adjust the score the students produced by reducing their score or raising their score. I take the difference in my score and the students' score and base the adjustment on this amount. If they were overly generous in their scoring I will take the difference and subtract it from my score as a penalty for not providing an honest evaluation. If the were too hard on themselves, I will adjust the score upward to a value halfway between their score and my score, in case I am being too generous. This process provides an incentive for students to consider honestly the quality of their work.

Afterward, I introduced the idea of bootstrapping a sample. I differentiate the idea of re-sampling for redistribution and re-sampling for bootstrapping with the idea that a bootstrap sample is developed from a single sample while a redistribution is used when there are two samples. I had seven students come up front as an example. I numbered the students and then randomly selected students seven times, using replacement. We measured the percentage of females in the sample. With this process, we can look at what random samples may look like if the units in the sample being used are somewhat representative of the population.

I then used the CPMP software to demonstrate how the software can generate these samples. I used a simple data set of four values: 10, 10, 10, and 50. After generating 10,000 bootstrap samples, a clear picture of what random samples look like appeared.

The bootstrap method is the last analysis technique that I will introduce for the semester. Students now possess three techniques to consider what randomness may look like and to use for determining probabilities for seeing real samples with these characteristics.

The next project was introduced to students. This project has students consider the issue of whether or not people wash their hands after using the restroom. I asked students to develop a question of interest, create a null and alternative hypothesis for their question, consider what data they would collect, how they would collect the data, and what types of analysis the would use.

The groups had quality discussions about what they would analyze, their hypotheses, and what data they would collect and the procedure they would use for collection. I told the students I would need to sign off on what they were doing before I would release them to test their collection procedure.

As I walked around, I helped groups to focus their question of interest. Some wanted to consider differences between hand-washing rates of males and females, others wanted to look at overall hand-washing rates, others wanted to examine the use of soap when washing. The hypotheses that were created were well thought out and consistent with the questions of interest.

The discussions around data collection were also good. I discussed randomness in their data collection. Most groups had already considered this facet of their collection process. As students considered the physical collection process, I would ask them how they could be sure that their collection technique would not influence the behavior of the subjects they were studying. This led to interesting discussions about how to conduct observations.

Most groups got to a point where they were ready to test their data collection techniques. Unfortunately, at the end of the class period there wasn't much in the way of traffic in the restrooms. We'll continue working on the study next class. Hopefully students will be able to test their data collection procedure and actually collect their data.

Visit the class summary for a student's perspective and to view the lesson slides.