Intro to Internet Science: A Postlude

All right, we did it! 10 topics, 10 weeks, and a whole slew of examples. I’ve had a lot of fun, gotten some great feedback, and had some very kind comments from some very lovely teachers. It’s also given me some good ideas for some ongoing posts.  In the talks I give I almost never have time to get in to any actual math, but hey, what’s the point of having a blog if you can’t go on and on about the stuff you like? I’ll probably be calling that “crazy stats tricks” and at a minimum I’ll cover some of the topics I complained about in Part 7.  Any suggestions for that series, or feedback on this series is welcome either in the comments or on the feedback page.

Now that I have that out of the way, lets take a moment to reflect on what we’ve learned, eh?  Overall, there are four P’s:


We spent a little time on all four:

Presentation: How They Reel You In
In Parts 1 and 2, we learned how quickly the internet spreads completely false information, and to always make sure what you are quoting is actually real. We also learned that headlines are marketing tools, and to be wary of what they are selling.

Pictures: Trying to Distract You
In Parts 3 and 4, we added some visuals. Narrative pictures, or those that help illustrate the story, can set impressions that can be ridiculously hard to correct. It gets even worse when you add graphs. Even a little bit of technical information can make things look more credible than they deserve.

Proof: Using Facts to Deceive
In Parts 5, 6, and 7, we covered “the truths people use to lie with”. Here we covered information that is true, but used to give false impressions. We started with stories and anecdotes, which are often used to humanize and emphasize various points. Next we moved on to experts and balance, and how we need to be careful who we listen to and who we dismiss.  Finally I gave a woefully short and incomplete overview of some statistical tricks that get used a lot.

People: Our Own Worst Enemy
And now we come to the part where we have only ourselves to blame. First we took a look at how our own pre-existing beliefs color our views of facts and even impact our ability to do math. Next, we take a look at how our tendency to not be entirely honest can screw up surveys and research based on them.  Finally, we had a bit of a discussion about the limits of scientific understanding, research ethics and things we may never know.

And that’s a wrap!

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