Stats Chat

Rasmus Bååth, who is doing a PhD in Cognitive Science, in Sweden, has written a web app that predicts blood alcohol concentrations using reasonably sophisticated equations from the forensic science literature.

The web page gives a picture of the whole BAC curve over time, but requires a lot of detailed inputs. Some of these are things you could know accurately: your height and weight, exactly when you had each drink and what it was. Some of them you have a reasonable idea about: is your stomach empty or full, and therefore is alcohol absorption fast or slow. You also need to specify an alcohol elimination rate, which he says averages 0.018%/hour but could be half or twice that, and you have no real clue.

If you play around with the interactive controls, you can see why the advice given along with the new legal limits is so approximate (as Campbell Live is demonstrating tonight).  Rasmus has all sorts of disclaimers about how you shouldn’t rely on the app, so he’d probably be happier if you don’t do any more than that with it.

Each week, we would like to invite readers of Stats Chat to submit nominations for our Stat of the Week competition and be in with the chance to win an iTunes voucher.

Here’s how it works:

• Anyone may add a comment on this post to nominate their Stat of the Week candidate before midday Friday August 8 2014.
• Statistics can be bad, exemplary or fascinating.
• The statistic must be in the NZ media during the period of August 2 – 8 2014 inclusive.
• Quote the statistic, when and where it was published and tell us why it should be our Stat of the Week.

Next Monday at midday we’ll announce the winner of this week’s Stat of the Week competition, and start a new one.

(more…)

The Cochrane Collaboration, the massive global conspiracy to summarise and make available the results of clinical trials, has developed ‘Plain Language Summaries‘ to make the results easier to understand (they hope).

There’s nothing terribly noticeable about a plain-language initiative; they happen all the time.  What is unusual is that the Cochrane Collaboration tested the plain-language summaries in a randomised comparison to the old format. The abstract of their research paper (not, alas, itself a plain-language summary) says

With the new PLS, more participants understood the benefits and harms and quality of evidence (53% vs. 18%, P < 0.001); more answered each of the five questions correctly (P ≤ 0.001 for four questions); and they answered more questions correctly, median 3 (interquartile range [IQR]: 1–4) vs. 1 (IQR: 0–1), P < 0.001). Better understanding was independent of education level. More participants found information in the new PLS reliable, easy to find, easy to understand, and presented in a way that helped make decisions. Overall, participants preferred the new PLS.

That is, it worked. More importantly, they know it worked.

• Nicely balanced Guardian article on genetic sequencing in rare diseases

Some diagnoses open up a whole world of support groups and treatments for patients who previously had no name to put to their disorder. But even when there is no treatment, no sudden rush of hope and relief, families are grateful for the diagnosis.

• Andrew Gelman:
  

  The result is what I call the “scientific surprise” two-step: (1) When defending the plausibility of your results, you emphasize that they are just as expected from a well-estabilished scientific theory with a rich literature; (2) When publicizing your results… you emphasize the novelty and surprise value.

• From the copyediting blog “Ten Minutes Past Deadline”, an example of how statistical errors avoid getting into print (on a good day, God willing and the creek don’t rise)

• Network visualisation game to show how a disease spreads (via Flowing Data).

“…Unlike people, computer programs aren’t embarrassed by their prejudices and don’t try to hide them.” Thomas Lumley discusses the algorithm-based tools that sift data about us and our behaviour in this week’s New Zealand Listener. Click here for the full story.

• Five data visualisation tools focused on design: once you can do ‘accurate’, the next step is ‘ooh, shiny’
• Why do people suddenly think their iPhones are slow? A piece at the NY Times ‘Upshot’ blog using Google search data
• Cathy O’Neill writes about two stories about standardized tests
• [update: I forgot to add] Unsurprising conclusions drawn from unreliable data: According to Stuff a bogus poll says people under 35 are more likely to use smartphones when looking for a job than people over 50.

The American Statistical Association is launching a public relations campaign to make people think statistics is less boring and pointless, which is good:

We want students and parents to have a better understanding of a field that is often unknown or misunderstood. Statistics is not just a collection of numbers or formulas. It’s not just lines, bars or points on a graph. It’s not just computing. Statistics is so much more. It’s an exciting—even fun—way of looking at the world and gaining insights through a scientific approach that rewards creative thinking.

That’s a quote from the  shiny new website, ThisIsStatistics. It has stories about what statisticians do, and information about salary and job trends and stuff.  There are videos of statisticians talking about their work: currently Roger Peng (Johns Hopkins, SimplyStatistics blog) and Genevra Allen (Rice University).

It’s slightly disappointing that more of the people on the site arent’ real, just stock photos, but I suppose that’s unavoidable. What’s a bit more annoying is one of the photos in particular:

This looks as if it was constructed specially (the cup/mat/tablet/glasses are stock, eg).  It’s a rose chart, which is an ok way to display circular data (eg wind directions), but is not so good for comparison because of the way the wedges change shape as they get larger. The numeric labels are also a slightly strange choice for a circle measured in degrees (90 isn’t a multiple of 20).

Much more importantly, given the emphasis of the site on statistics as solving real problems, this is labelled as not being real: “data A” and “data B”.  Not helpful when we’re trying to tell people “It’s not just lines, bars or points on a graph”.

Team Ratings for Round 21

The basic method is described on my Department home page. I have made some changes to the methodology this year, including shrinking the ratings between seasons.

Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Performance So Far

So far there have been 144 matches played, 79 of which were correctly predicted, a success rate of 54.9%.

Here are the predictions for last week’s games.

Predictions for Round 21

Here are the predictions for Round 21. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

Team Ratings for the Super Rugby Final

The basic method is described on my Department home page. I have made some changes to the methodology this year, including shrinking the ratings between seasons.

Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Performance So Far

So far there have been 124 matches played, 82 of which were correctly predicted, a success rate of 66.1%.

Here are the predictions for last week’s games.

Predictions for the Super Rugby Final

Here are the predictions for the Super Rugby Final. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

An excellent piece from sports site Grantland (via Brendan Nyhan), on finding explanations for random noise and regression to the mean.

As a demonstration, they took ten baseball batters and ten pitchers who had apparently improved over the season so far, and searched the internet for news that would allow them to find an explanation.  They got pretty good explanations for all twenty.  Looking at past seasons, this sort of short-term improvement almost always turns out be random noise, despite the convincing stories.

Having a good explanation for a trend feels like convincing evidence the trend is real. It feels that way to statisticians as well, but it isn’t true.

It’s traditional at this point to come up with evolutionary psychology explanations for why people are so good at over-interpreting trends, but I hope the circularity of that approach is obvious.