April 28, 2014

Stat of the Week Competition: April 26 – May 2 2014

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 May 2 2014.
  • Statistics can be bad, exemplary or fascinating.
  • The statistic must be in the NZ media during the period of April 26 – May 2 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.

The fine print:

  • Judging will be conducted by the blog moderator in liaison with staff at the Department of Statistics, The University of Auckland.
  • The judges’ decision will be final.
  • The judges can decide not to award a prize if they do not believe a suitable statistic has been posted in the preceeding week.
  • Only the first nomination of any individual example of a statistic used in the NZ media will qualify for the competition.
  • Individual posts on Stats Chat are just the opinions of their authors, who can criticise anyone who they feel deserves it, but the Stat of the Week award involves the Department of Statistics more officially. For that reason, we will not award Stat of the Week for a statistic coming from anyone at the University of Auckland outside the Statistics department. You can still nominate and discuss them, but the nomination won’t be eligible for the prize.
  • Employees (other than student employees) of the Statistics department at the University of Auckland are not eligible to win.
  • The person posting the winning entry will receive a $20 iTunes voucher.
  • The blog moderator will contact the winner via their notified email address and advise the details of the $20 iTunes voucher to that same email address.
  • The competition will commence Monday 8 August 2011 and continue until cancellation is notified on the blog.

Rachel Cunliffe is the co-director of CensusAtSchool and currently consults for the Department of Statistics. Her interests include statistical literacy, social media and blogging. See all posts by Rachel Cunliffe »


  • avatar
    William Eaton

    Statistic: “first-born girls were 13 per cent more ambitious than first-born boys”
    Source: NZ Herald
    Date: 28-Apr-2014

    Not clear how we can measure that so accurately.

    Happily, the story quickly devolves into random examples that undercut the headline: First-born best placed to succeed.

    3 years ago

  • avatar
    Nick Iversen

    Statistic: A 50% probability is useless
    Source: New Zealand Herald
    Date: 29 Apr 2014

    I know it’s not the done thing to criticise university professors on this university website and that this post is therefore ineligible for the prize but the error should be pointed out so here goes.

    In the article Chris de Freitas says that a 50% probability is not useful because it “is no better than flipping a coin.”

    This is faulty reasoning. The NOAA say (http://www.elnino.noaa.gov/lanina_new_faq.html) that “El Niño and La Niña occur on average every 3 to 5 years” so in any one year the probability of an event is 0.20 to 0.33.

    So a notice of a 0.50 probability means that the event is more likely to occur than usual. This is useful information for farmers. The fact that the probability is the same as a coin toss is irrelevant.

    He says “it can lead to an impression that skill exists when it does not.” Turning a 0.20 prediction into a 0.50 prediction IS an indication of skill.

    If I could predict lotto numbers with a 50% probability (which is no better than a coin toss) I’d be filthy rich.

    He goes on to say “send an email to, say, 512 people with the message that there is a 50 per cent chance of an El Nino developing over the coming summer. Of these, 256 should get the correct prediction.”

    That doesn’t make sense. All 512 people got the same email. So 256 can’t be different.

    What he means is that if you send 256 emails predicting an event will happen and 256 emails predicting an event won’t happen then 256 people will get the correct prediction. So what? That doesn’t prove anything about 50% probabilities. I could have chosen 12 people and 500 people. That wouldn’t prove anything either. Some people would get correct and some incorrect predictions. So what?

    3 years ago