Monthly Seminars

No difference does not imply equivalence: misuse of P values in equivalence/non-inferiority testing

There has been growing interest in studies to determine if new therapies have equivalent or non-inferior efficacies to standard therapy. These studies are called equivalence/noninferiority studies. This talk will describe the concepts and statistical methods involved in testing equivalence/non-inferiority, and its difference to superiority testing. We will demonstrate with examples the setup of specific margins and null hypotheses, the use and interpretation of confidence intervals as well as how to avoid the misuse of P values in equivalence/non-inferiority testing.

The talk will be about 30 minutes long and will be followed by time for discussion. We'll move on after our talk to Hacky Hour (Penny Lane Café, 3.00 pm to 4.00 pm) where people can get help on statistics and bioinformatics, high performance computing and other data-related things.

Speaker:   Zhixin Liu, Statistical Consultant, Stats Central. 

Date and Time:  Thursday, 8th August 2019, 2.30 pm to 3.00 pm  Please note new starting time for our seminars!

Location:  Central Lecture Block, Theatre 5, Kensington Campus, UNSW Sydney

Please book using the button below (it's free!).

Dealing with missing data in your research

Missing data occurs in almost all research, even well-designed and controlled studies. Missing data can reduce power and result in a biased estimate of your effect of interest. In this talk, I will review the mechanisms that give rise to missing data.  I will also discuss some of the strategies available to address missingness, such as value substitution and deletion and more advanced methods such as imputation and maximum likelihood.

The talk will be about 30 minutes long.

Speaker:  Nancy Briggs, Senior Statistical Consultant and Manager, Stats Central. 

Date and Time:  Thursday, 11th July 2019, 2.30 pm to 3.00 pm  Please note new starting time for our seminars!

Location:  Central Lecture Block, Theatre 1, Kensington Campus, UNSW Sydney

Visualising data - making sure your graph is worth 1,000 words

How can you make a picture of your data to make its message clear? You need good graphs to analyse data well and communicate your results effectively. This talk focusses on principles of effective data visualisation. Graphing principles and different types of graphs will be demonstrated using the R statistical package, but the basic principles apply to making graphs in any software package.

The talk will be about 30 minutes long and followed by discussion.

Speaker: Peter Geelan-Small, Statistical Consultant, Stats Central.

Date and Time:  Thursday, 13 June 2019, 3-4 pm

Location:  Central Lecture Block (E19), Theatre 6, Kensington Campus UNSW Sydney

Slides for the presentation are HERE.

Three C's of causal consideration: confounding, collinearity and colliders

A useful talk about deciding on the inclusion/exclusion of variables based on certain causal relationships or large levels of correlation.

The talk will be about 30 minutes long and followed by discussion.

Speaker:  Ben Maslen, Statistical Consultant. Ben works at the interface of statistics and ecology using a wide variety of statistical techniques and has particular expertise in models for multivariate abundance data.

Date and Time:  Thursday, 23 May 2019, 3-4 pm

Location:  Central Lecture Block (E19), Theatre 3, Kensington Campus UNSW Sydney

Slides for the presentation are HERE.

How do you deal with count data?

We will talk about techniques for dealing with data obtained when we count things and various properties that these types of data have. Some topics we will discuss are: mean-variance relationships, overdispersion and underdispersion, as well as a variety of models for data obtained from counts (such as, Poisson and negative binomial models and models for binomial successes).

The talk will be about 30 minutes long and followed by discussion.

Speaker:  Ben Maslen, Statistical Consultant. Ben works at the interface of statistics and ecology using a wide variety of statistical techniques and has particular expertise in models for multivariate abundance data.

Date and Time:  Thursday, 11 April 2019, 3-4 pm

Location:  Central Lecture Block (E19), Theatre 1, Kensington Campus UNSW Sydney

Slides for the presentation are HERE.

What can Data Science do for you?

Ever wonder how investment companies improve investment returns? What tools manufacturers use to improve their productivity? How e-commerce companies can increase their revenue? (Spoiler alert: they use Data Science!)

This is an introductory seminar on Data Science from a Computer Science perspective. Data science is a multi-disciplinary field that requires skills from mathematics, computer science and business. I will cover topics including:

• The basics of Data Science
• How to apply Data Science to your projects and
• Requirements and challenges when using Data Science

The talk will be about 30 minutes long

Speaker: Raymond Wong, Stats Central and Associate Professor at the School of Computer Science and Engineering. Raymond's research interests include: big data management, XML and semi-structured data, data mining and analytics, mobile technologies and service computing

Date and Time:  Tuesday, 12th March 2019, 3.00 to 4.00 pm

Location:  NewSouth Global Theatre, Webster Building (G14), Room 127 | UNSW Kensington Campus

Slides for the presentation are HERE

Analysis of Pretest-Posttest Data: It’s not as straightforward as you might think!

Which stats software should I use?

Anyone who has to do some stats needs to first make the decision about what software to use - SPSS?  R?  Stata?  In this short talk we will work through some of the things to consider in choosing a package, and some of the recent trends in package use in research and in industry.

The talk will be about 30 minutes long and will be followed by (free!) afternoon tea and time for discussion.

Speaker: David Warton, Director of Stats Central, UNSW (Professor of statistics specialising at its interface with ecology. He has been wondering what stats package to use for some time and has usually just followed the advice of people who sound like they know what they are talking about.)

Date and Time:  Thursday, 8th November 2018 - 3.00 pm to 4.00 pm

Introductory Meta-Analysis

Systematic reviews and meta-analyses are appearing increasingly often. We will discuss systematic reviews and data collection and also effect sizes, heterogeneity and publication bias. Fixed and random effects meta-analyses will also be compared. We will include examples of analyses, forest plots, funnel plots and interpretation of results.

The talk will be about 30 minutes long and will be followed by (free!) afternoon tea and time for discussion.

Speaker: Zhixin Liu, Stats Central, UNSW

Date and Time:  Thursday, 11th October 2018 - 3.00 pm to 4.00 pm

Regression to the mean and how to deal with it

Despite being “transparent to the point of being obvious”, the phenomenon of regression to the mean – first recognised in the 1800s – is still catching out researchers. We’ll look at what regression to the mean is, how to identify it, how you can reduce it at the design stage and statistical methods you can use to account for it at the analysis stage of your pre-post experiment.

Analysing small data sets - a mini overview

Analysing data sets with a small number of observations can be a challenge! This seminar will touch on some of the problems that arise in these analyses and outline a number of analysis methods, together with their limitations. The general types of method we will talk about are parametric (e.g. t test, ANOVA), non-parametric (e.g. Mann-Whitney, Kruskal-Wallis) and randomisation methods.

The talk will be about 30 minutes long and will be followed by (free!) afternoon tea and time for discussion.  Please register by Thursday, 12th July, 10.00 am, so we can cater properly for afternoon tea!

Intraclass Correlations - How to use them for reliability and why there are so many of them

Before a measure can be used for research or application purposes, the measure itself must be shown to be replicable across measurements.  Intraclass correlations (ICC) are one way to assess reliability.  This seminar will discuss the calculation of ICCs, how to report the results and show why there are so many different versions.

Four strategies for dealing with multiple comparisons

Multiple hypotheses may be generated by

• multiple treatment arms;
• heterogeneous treatment effects;
• measuring multiple outcome variables.

In a hypothesis testing framework, using p < 0.05 as a criterion for declaring significance, it can be easy to get results that are significant by chance when many hypotheses are tested. This talk will discuss four things you can do when faced with multiple comparisons and will cover:

1. the difference between controlling the family-wise error rate and the false discovery rate;
2. the Bonferroni-Holm adjustment;
3. the Benjamini-Hochberg adjustment;
4. strategies for multiple outcome variables and strategies for multiple comparisons which are correlated.

The talk will be about 30 minutes long and will be followed by (free!) afternoon tea and time for informal discussion.

Speaker: Eve Slavich, Stats Central, UNSW

Date and Time:  Thursday, 10th May 2018 - 3.00 pm to 4.00 pm