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Mr Epidemiology

No, I'm not a skin doctor

Month

December 2013

The Biggest Sci-Ed Stories of 2013

As 2013 comes to an end, it’s a time for reflection and thought about the last year, and look towards to the future. 2013 was quite the year in science, with impressive discoveries and wide reaching events. I’ve selected my five favourite science stories below, but I welcome your thoughts and would love to hear your thoughts on the top science stories of 2013.

GoldieBlox and Diversity in Science
This isn’t a new issue by any stretch, but it is one of the most important issues facing science (and higher education in general). Diversity in science is essential for a number of reasons, but perhaps most importantly, it gives us different perspectives on problems, and thus, new and novel solutions. Within the scientific establishment, there have been many stories about discrimination and inappropriate conduct (see SciCurious’ excellent series of posts on the matter, including posts by friends of the blog @RimRK and @AmasianV), and, unfortunately there are no easy solutions.

Perhaps the biggest diversity-related story this year was GoldieBlox. While initially this started as a media darling (who didn’t love the video?), further examination revealed deep-set problems in how they chose to approach the issue of gender representation in STEM disciplines.

There is a lot of change required to reach equality in science careers and to ensure that people are judged and given opportunities based on their work, not their privilege. Lets hope that in 2014 we can start the ball rolling on that change.

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The Biggest Public Health Stories of 2013

2013 was a big year for public health. We were thrust to the forefront again with disease outbreaks, and have had to deal with increased skepticism of the nature of what we do from the public. Meanwhile, within the establishment, rifts have been growing between groups, as different professional organizations vie for power and control. Here are my top five public health stories for 2013, presented in no particular order, but I’d love to hear yours in the comments.

1. Polio in Syria
Polio is a crippling disease that has been covered on the blog before. It’s been almost completely eradicated, but is still endemic to certain parts of the word. However, following civil unrest in Syria, polio has started to spread again and has, to date, crippled 17 children. Before the March 2011 uprising, vaccination rates were estimated to be above 90%. However, since then, estimates for vaccination rates hover around 68% – enough to prevent the benefits of herd immunity from kicking in. In order to increase immunization rates, the UN is trying to mobilize a vaccine drive. However, due to political and safety concerns, they are having a hard time ensuring that all children are vaccinated. To quote NPR:

Polio does not stop at borders or military checkpoints. Without a comprehensive response to stop the virus, aid workers fear that the outbreak could become a public health catastrophe.

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Using Math to make Guinness

William Sealy Gosset, statistician and rebel | Picture from Wikimedia Commons

Let me tell you a story about William Sealy Gosset. William was a Chemistry and Math grad from Oxford University in the class of 1899 (they were partying like it was 1899 back then). After graduating, he took a job with the brewery of Arthur Guinness and Son, where he worked as a mathematician, trying to find the best yields of barley.

But this is where he ran into problems.

One of the most important assumptions in (most) statistical tests is that you have a large enough sample size to create inferences about your data. You can’t make many comments if you only have 1 data point. 3? Maybe. 5? Possibly. Ideally, we want at least 20-30 observations, if not more. It’s why when a goalie in hockey, or a batter in baseball, has a great game, you chalk it up to being a fluke, rather than indicative of their skill. Small sample sizes are much more likely to be affected by chance and thus may not be accurate of the underlying phenomena you’re trying to measure. Gosset, on the other hand, couldn’t create 30+ batches of Guinness in order to do the statistics on them. He had a much smaller sample size, and thus “normal” statistical methods wouldn’t work.

Gosset wouldn’t take this for an answer. He started writing up his thoughts, and examining the error associated with his estimates. However, he ran into problems. His mentor, Karl Pearson, of Pearson Product Moment Correlation Coefficient fame, while supportive, didn’t really appreciate how important the findings were. In addition, Guiness had very strict policies on what their employees could publish, as they were worried about their competitors discovering their trade secrets. So Gosset did what any normal mathematician would.

He published under a pseudonym. In a startlingly rebellious gesture, Gosset published his work in Biometrika titled “The Probable Error of a Mean.” (See, statisticians can be badasses too). The name he used? Student. His paper for the Guinness company became one of the most important statistical discoveries of the day, and the Student’s T-distribution is now an essential part of any introductory statistics course.

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