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A Call for More Statistical Thought?

This article from Newsweek talks about what our science students really need to be learning. Here is a taste:

This column is about science education, but teachers and curriculum designers should click away now rather than risk apoplexy. Instead of making the usual boring plea for more resources for K–12 science (or, as it is now trendily called, STEM, for science, technology, engineering, and math), I hereby make the heretical argument that it is time to stop cramming kids’ heads with the Krebs cycle, Ohm’s law, and the myriad other facts that constitute today’s science curricula. Instead, what we need to teach is the ability to detect Bad Science—BS, if you will.

The reason we do science in the first place is so that “our own atomized experiences and prejudices” don’t mislead us, as Ben Goldacre of the London School of Hygiene & Tropical Medicine puts it in his new book, Bad Science: Quacks, Hacks, and Big Pharma Flacks. Understanding what counts as evidence should therefore trump memorizing the structural formulas for alkanes. …

Few things mislead us more than failure to grasp simple statistical principles. …

The most useful skill we could teach is the habit of asking oneself and others, how do you know? If knowledge comes from intuition or anecdote, it is likely wrong. For one thing, the brain stinks at distinguishing patterns from randomness (no wonder people can’t tell that the climate change now underway is not just another turn in the weather cycle). … Science is not a collection of facts but a way of interrogating the world. Let’s teach kids to ask smarter questions.

Sounds to me like a call for adding statistical thought to the early science education of our students. This is also why programs like MythBusters, while fun, can be somewhat misleading. The science on the show are general samples of size one. Every once in a while the importance of variability is acknowledged, and sometimes they even take multiple observations to try to estimate the variability, or “noise” in the data. I love the show, and what they test, but I do wish they would read this article and add getting more data as a priority. They need not show every rep, just show an example or two, and comment on how many reps they did. Sure, the cost would be prohibitive in some cases, but doing this when they can would teach their viewers something valuable about the scientific process: You need data to make conclusions, and more data is better if it is gathered well!

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