When I taught these two lectures in 2019 I was able to do so in person, this year I taught online via Zoom and so I had to make some changes.
I found myself wondering, What the heck do Arts students want to know about Statistics? But then I realised that I was thinking about Arts too narrowly and these students might be interested in a pathway in history, English, environment and society etc. These were all areas where some basic understanding of Statistics would be useful. I also remembered that statistical literacy is a fundamental skill for anyone, no matter their field. I wanted to keep the content interesting and engaging without getting technical, and I only had two lectures in which to cover everything I wanted to.
This year I titled my lectures 7 handy tips that will change how you consume media forever – What you learn in this lecture might surprise you! I went with a purposefully click-bait title as this led to some great discussion around how consumers are encouraged to read articles. I’ll summarise my seven handy tips and how I taught about them below:
1. Practice safe stats: I began by getting ideas from students of what data had been collected about them (or their behaviour) in the last week. The students put their ideas into the chat box on Zoom and while they started slowly they soon began to flood in. I then gave students some more ideas that they hadn’t come up with and we talked about how to protect your data and to be more aware of what data is being collected about you and what it might be being used for. We also briefly covered some ideas surrounding the ethics of data collection and ownership.
2. Data beat anecdotes: For this I first had students read this article (http://www.nbcnews.com/id/33402802/ns/health-kids_and_parenting/print/1/displaymode/1098/) and discuss, in breakout rooms, what they thought about the article and whether or not they would, as a parent, choose to allow their child to watch a scary film alone. When students rejoined the lecture I asked students to share some of what they’d discussed and to identify parts of the article that were the most convincing for them. We then went through the article and marked each part as either, research, expert comment, or anecdote. We saw that the article was quite well written with the research and expert comments more common near the start and the anecdotes more common towards the end. We also discussed how it was the anecdotes that the students had found more relatable and therefore more convincing.
3. Check the source: Here I presented various different articles on the outcomes of various polls. We discussed how to look for the source of the data and how to evaluate the credibility of the source. I showed students that there were many websites that could help them to check possible fake news (such as politifact, factcheck.org, snopes.com etc). I showed students this article from StatsChat.org (https://www.statschat.org.nz/2017/05/26/big-fat-lies/) which shows obesity data for the OECD countries but highlights that some of that data is measured and some is self-reported!
4. Consider the lurking variable: Our first lecture concluded after the previous section and students were given time to ask questions. One student commented on how some of the issues we had already seen were a mix up of correlation and causation. This was a great link into our next lecture which was about just that. I played the students the start of this video (https://youtu.be/VMUQSMFGBDo) and we discussed how (obviously) ice cream does not cause forest fires or an increase in crime but what we were really seeing was a correlation. I discussed with students how two things could be related without one necessarily causing the other one and often this meant that there was a third (lurking) variable that was doing the causing. In the case of the ice cream this was the weather. I put students into breakout rooms again to discuss what they think might be going on with the claim made in this article ( https://www.nbcnews.com/health/womens-health/sleeping-tv-may-lead-weight-gain-study-suggests-n1015816) that sleeping with the TV on leads to weight gain. Students then rejoined the main lecture to share their ideas. In spite of this being quite a difficult concept for students there were a lot of really good ideas presented and we talked about how each “lurking variable” might impact on both weight gain and on the likelihood of someone sleeping with the TV on.
5. Variation is everywhere: I used another StatsChat.org article as the basis for this next talking point (https://www.statschat.org.nz/2017/05/04/summarising-a-trend/). With the election coming up in NZ there are lots of political statements flying back and forth in the media at the moment, so looking at this one from the previous election was interesting for the students. From looking at the graph showing the number of young people not earning or learning between 2008 and 2016 we could see that the statement made by Labour that Under National, the number of young people not earning or learning has increased by 41% was technically true but not an accurate reflection of what was really going on. We could see that there was always a variation in the numbers, up and down, and that to make their claim Labour had picked the lowest point as their starting value and the highest point as their ending value.
6. The media make small things big: For this one I turned to two friends, Hans Rosling and John Oliver. I first (using a Zoom poll) asked the students two questions from Hans Rosling’s Gapminder ignorance survey: What is the life expectancy of the whole world? 50, 60 or 70? and In the last 20 years has the proportion of people living in extreme poverty worldwide doubled, halved or stayed the same? Unsurprisingly the students answered (mostly) incorrectly on both questions, taking a more negative viewpoint than the reality. Why was this? Because we don’t often hear stories documenting the slow and steady progress of the world but we do hear the big sensational stories documenting disasters and diseases. I then showed part of this episode of Last Week Tonight with John Oliver (https://youtu.be/0Rnq1NpHdmw) where he talks about how small research findings can get twisted and manipulated into huge media stories.
7. Data reflect values: For their final task I put students back into their breakout rooms and had them try to define “living in poverty”. Their definition had to be clear and precise enough that it could be used to categorise any person living in New Zealand. When students returned there were a variety of different definitions ranging from those based on the ability to afford certain basic needs to those based on income. We discussed how different definitions had their pros and cons, some were flexible and would work in any country or at any time (e.g. earning less than 25% of the average income for a region), some reflected the cost of living (e.g. unable to purchase specified goods and services deemed necessities). We then discussed how different definitions reflect the social and political values of those who come up with the definition. We talked about how homelessness was defined and how those in temporary housing or unsafe housing might be considered homeless by some definitions but not by others.
After both of these lectures the students had an opportunity to ask questions. We stopped the recording and asked them to turn their cameras on. The quality of the student questions showed they had been really thinking about what we had discussed and I was quite pleased. While I definitely prefer teaching in person I think through making use of the chat box and the breakout rooms I managed to engage foundation Arts students in important statistical thinking, and for me that’s a win!
