Nov 142017
 

Two recent articles add to the list of materials that students in my lab should ponder.

The first deals with limitations of statistics in science, or at least, limitations in our understanding and application of statistics. This is an on-going topic for our data scientists to track.

A NYT article on NSA Shadow Brokers is especially worthy of your consideration, since so many of our present projects involve analysis and prediction of security-related properties.

To see how the above two readings are modestly related to one another, think about what data are used to predict opportunities to penetrate a site, what data predict potential intrusions over time, and what data are used to track uses of exfiltrated materials. Then … think about whether the science behind each is equally-well developed or applied. What limits someone performing those activities and how would scientists offer that person stronger tools? There are some great research activities lurking in the answer to that question.

 Posted by at 7:54 am on November 14, 2017
Nov 062017
 

I often talk with my students about bias when we are conducting a research project. Usually this discussion launches with me getting on a soap box to throw around words like old school methods and sound experimental design (also met with small eye rolls they think I don’t notice.) My extended rant will cover a lot about how to conduct risk reduction exercises, which after a fashion resemble agile development methods except in hacking our insights instead of code. Overall we’re pretty interested in knowing how to make good engineering decisions on use of our time; we want the greatest illumination for least cost on each step along the way as we converge to results.

With that in mind, a nice read about bias is The Trouble With Scientists. This reminds us that while there are the things we want to know, we need discipline to ensure we’re not just cherry picking data to support a conclusion we already want to reach; we need discipline to force ourselves to look coldly at what we don’t know. These go hand in hand.

 Posted by at 3:47 pm on November 6, 2017
Jul 062017
 

We love tracking the unwelcome consequences of policy decisions that are made under a banner of righteousness but which bring surprises to those who weren’t listening to scholars who tried to point out what was in the fine print. This one’s a doozy.

National Review points out Discarded solar panels are piling up all over the world, and they represent a major threat to the environment. If you only measure the value of solar power from after the panels are up and before they come down, then probably there is a net plus – plus or minus those awkward moments when the sun isn’t shining of course. If you only drive forward with that in mind, the surprise waiting you is a net loss to our environment’s health, since the cost of procuring the more exotic materials needed for these panels is great (a lot more waste water, a lot of pollution) and the discarded panels pile up rather than become recycled. (Also batteries, this is not a prime consideration in the linked article.)

Scholars would want to objectively weight the lifecycle properties and make sound decisions; cherry picking your results is something you only do to justify outcomes you’ve already figured out. That may be good for your wallet if you’re in the enviro business, but it isn’t necessarily good for the environment.

 Posted by at 8:19 am on July 6, 2017
Nov 012016
 

A nice article at Discover Magazine tells some of the many ways our data are used. None of this is particularly surprising to privacy advocates, of course, but this is an interesting perspective since the author speaks from the algorithmic point of view; we of course would have wondered why we allowed data to be available for those algorithms to be used as such in the first place.

 Posted by at 7:29 am on November 1, 2016
Jun 292016
 

Clicking ‘I accept’ doesn’t mean you surrender right to know how a company uses your data“. That’s the title of an article about some of the many ways companies use your data, often without your full appreciation of what you have given away.

As described in the linked article, some people are working hard to help you and fellow consumers be more informed about the effect of those disclosures. Bravo!

 Posted by at 4:56 pm on June 29, 2016
May 092016
 

… and not that other stuff. That’s effectively what Facebook provided, according to “news curators” who had worked there and were interviewed for a Gizmodo article. The effect was to put a thumb on the scales of public opinion, biasing it toward promotion of liberal views and suppressing material that might have reflected conservative opinion, or so was the article’s point.

And that point would be quite plausible when you have an unchecked system that relies upon promotion of articles by people who are drawn from a pool that itself is dominated by certain views. Selection bias nuances the choice of curators, and the curators thus bias the messaging by what they choose to promote. How do they recognize a likely article? They’ll see it when they know it.

 Posted by at 10:08 am on May 9, 2016
Feb 242016
 

Bad science (and sometimes difficult science done poorly) happens all the time, and periodically we re-blog examples as reminders. This edition’s sampling is titled for a truism quoted in the first article about ways sham scientists plump up their own work and try to dominate in the literature: “The amount of energy necessary to refute bullshit is an order of magnitude bigger than to produce it.”

In the ‘difficult science done poorly’ category we have another reminder from a nice piece in the Atlantic about challenges in reproducibility of studies.

But what’s a reminder like this without one of the old standby topics, the publication of utter drivel.

Let’s be a bit more discriminating out there when it comes to what we might believe in the literature.

 Posted by at 6:22 pm on February 24, 2016
Jan 062016
 

… or not depending on how you use it, as we’re reminded in this article Bayes’s Theorem: What’s the Big Deal?. But then, we probably knew that already. Nevertheless, it is a nice discussion about some of the aspects of reasoning about processes using probability, and reminds us that there are real philosophical differences between Bayesian and frequentist statisticians.

 Posted by at 7:58 pm on January 6, 2016