About “Learn and teach Mathematics and Statistics”

The aim of Statistics Learning Centre is to help people learn and teach maths and stats more effectively and more enjoyablye.

This blog gives ideas to help teachers and learners of Mathematics and Statistics (and Operations Research.) These disciplines can be difficult to teach and understand. The emergence of computer packages have changed the way these subjects can be taught, potentially making them more accessible and hands-on. There are many good blogs about the application of statistics but few about teaching these inherently interesting subjects. This blog aims to fit in that gap, entertaining, inspiring and helping teachers and learners alike.

The philosophy is that of inclusion, rather than protectionism.

Introducing Dr NicDr Nicola Ward Petty

Dr Nicola Petty is co-director of Statistics Learning Centre. She taught business statistics and operations research at the University of Canterbury in Christchurch, New Zealand for over twenty years, up until 2012. She was awarded a university teaching award in 2006 and has mentored other faculty, and developed innovative and successful courses to help people who find quantitative subjects difficult.

In 2008, with the help of her son, William, Dr Nic began to create videos for her class, teaching aspects of Excel and statistics. She put them on YouTube for her students to gain easy access. However the audience went much wider. One of the videos, “Understanding the p-value” became popular, gaining over 250 views a day and helping people all over the world to … understand the p-value!

More recently Dr Nic and William have been re-creating their older videos and making new ones, which you can see on the Creative Heuristics YouTube channel.

Dr Nic is an accomplished presenter, making statistical analysis accessible to a wide range of people. She has given presentations for groups such as physiotherapists, teachers and speech/language therapists, to help them in their understanding of the the role and method of statistical analysis.

You can follow Dr Nic as @rogonic on Twitter.


15 thoughts on “About

  1. Dr. Nic,

    I found out about your blog from Michael Trick’s page. Because you’re interested in statistics, particularly since your mission is to make probability stats and OR available to the masses, I thought I’d share my page with you, and in particular my latest script which helps understand Bayes’ Theorem. I have a similar mission, but more about all mathematics and the algorithmic way of thinking..

    I recently finished a script to help students understand Bayes’ Theorem. It is available at http://www.learninglover.com/examples.php?id=26.

    The script works as follows:
    – A set of mutually exclusive sets is randomly generated (the number of sets also varies). These sets are called Bi for i [in] [0, …, n].
    – A set A is randomly generated from the union of the Bi’s.
    – A table is displayed showing:
    Pr(Bi) for each i on line 1.
    Pr(A | Bi) for each i on line 2.

    – The user is given the option to select which of the mutually exclusive sets they would like to use to calculate the probability that this set caused the event A.
    – Once a set is chosen, the user clicks the “Calculate Conditional” button and Bayes’ Theorem gives the result.
    – If the “show work” checkbox was checked, then the steps used in this calculation are also shown.
    – All work is done using fractions to give an idea of where the numbers come from.


  2. Hi Nicola,

    I came across your blog and wondered if you would be interested in featuring the article I wrote about Operational Research. We are a recruitment company and recruit for talented O.R consultants. I thought this article may be of interest to your readers. Do let me know if you would like to feature this article.


    Many Thanks,

    Aleyx Perks


  3. Dear Dr. Nic,

    Greetings from far off North America. I came across your page while googling for statistics apps on the iPad. I will be sure to take a look at your videos. Though I dwell in far off Memphis I hail from Wellington originally and graduated from Vic. as an undergrad.


  4. Dear Nicola,

    Recently I came across your wonderful website. You might be interested to learn that the free educational software package ORSTAT with many modules on probability, statistics and operations research can be downloaded from my homepage

    http://personal.vu.nl/h.c. tijms

    Greetings from Holland Henk Tijms emeritus professor at the Vrije University in Amsterdam


  5. Pingback: My Online Statistics Education Experience | Math Encounters Blog

  6. Hi Dr. Nic. Is there anyway I could get consulting from you or from someone you recommend for public health data? Is there an email you can provide. And finally, I have a quick question on the question related to ordinal data….I know you mentioned that sometimes a mean is done for them but the people should keep in mind the imprecision of what a mean means in that context. In the same way, can a standard deviation be calculated for ordinal data?


  7. Oh, I forgot to click on “notify me of new comments via email” But i did on this one…so please respond to this comment also so I know you responded. -Omer


  8. I have a really crazy question on one of the easiest concepts in statistics. I’m somewhat embarrassed to ask it publicly, as I’ve had several upper division and graduate level statistics courses. But none of my professor have been able to explain this well. When doing surveys, which use a Likert scale, why do we always set the sample size using the standard equation for a proportion. where n=[(Za/2)^2 * (P(P-1))] /E^2 . I understand why that equation works when you have two proportions. However, with a Likert scale you may have several proportions, 5 to 10 typically. Each answer being mutually exclusive. So, mathematically, why does the simple proportion formula work in these cases? I already understand why assuming 50/50 is your best bet when you have unknown proportions in a binary outcome. However, I’m not satisfied with the answers I am getting on why this equation still yield a correct sample size for a liker scale with 5 to 10 exclusive choice variables. Thanks!


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