Introductory Statistics for Researchers

Welcome to Stats Central!Stats Central is a statistical consulting unit established at UNSW in 2016 to provide university-wide support for staff and students during study design and analysis.  It is funded through the research division and a growing suite ofpartner schoolsto provide support for UNSW research students and staff in collaborative and consultative roles.  We are currently staffed by six professionally accredited consultants with a breadth of expertise working with researchers across disciplines, and located in the Mark Wainwright Analytical Centre.  Stats Central offers:- Statistical consultations forUNSW staffandstudentsregarding all aspects of the research project (study design, analysis and communication).  Study design advice is free for all UNSW researchers and all consulting services are free for UNSW higher degree research students.  Book a consultationnow.- Intensive short courses on introductory statistics, study design, and regression analysis, at heavily reduced rates for UNSW staff and students.-Grant development and review: Free advice on study design and analysis in the project planning stages, advice on how to write details into your grant application from experienced statisticians, and review of draft applications.  For details write toStats.Central@unsw.edu.au- A hub for biostatisticians and statistical consultants around campus.Is your school or unit interested in partnering with Stats Central?Partner schoolsmake a cash contribution to the scheme in exchange for having a professionally accredited statistician based in their school part-time for enhanced research support.

Stats Central, UNSW Sydney

<H2 CLASS="MsoNormal">Note for UNSW attendees </H2>
ALL UNSW attendees need to pay course fees. If you would like to pay by internal transfer through your school, please choose the appropriate "Free" ticket to register for the course and then arrange with your department to pay the course fee. (This does not mean the course is free. We have to use the "Free" ticket label because of the Eventbrite site limitations.) If you would like to pay immediately, please choose the appropriate "Credit Card Payment" ticket.
 
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<H2 CLASS="MsoNormal">Course Overview</H2>
In many disciplines, researchers wishing to publish are asked to provide a rigorous statistical analysis. Reviewers are often specific about what statistical measures they want included. "Why wasn't Fisher's Exact Test used?" "Was an appropriate sample size determined a priori?"
Statistical analyses require specialised software to perform calculations. In this course, we use the free statistical program R, although researchers may have another statistical package available to them.
How does one decide which statistical procedure is the most appropriate? What do all the pages of the printout mean?
This course is designed as an introduction to statistical design and analysis for researchers. There is emphasis on understanding the concepts of statistical procedures (with a minimum of mathematics, although some will be discussed) and on interpreting computer output. This course is designed to help you, the researcher. It is helpful if you have done an undergraduate statistics subject, although this course can serve as a first introduction or a refresher.
Instructions on how to obtain computer printouts will be provided with an emphasis on interpreting the computer printout (most packages produce similar printouts). There will be plenty of practical work throughout the course.
Do you need to have previous experience using the program R?
It would be very helpful if you have some basic knowledge of R. We recommend our one-day course, Introduction to R, which runs just prior to this course. However, previous experience with statistical packages like SAS, SPSS or Stata and some basic programming skills are also helpful.
In this course, the statistical software package used is R exclusively. We do not use or demonstrate SPSS.
<H2 CLASS="MsoNormal">Course Outline</H2>
Introduction
Types of experiments, scales of measurement, which method to use.
Summarising and Graphing Data
Ways of presenting data (histograms, boxplots), measures of centre and spread, analysing tables, correlation, and confidence intervals.
Comparing Groups
Hypothesis testing concepts - power, significance, P-value. Comparing two groups (t-tests, Wilcoxon). 
Comparing many groups (with one treatment factor) - one-way ANOVA or Kruskal-Wallis - multiple comparison tests.
Finding Relationships
Correlation, predicting relationships (regression - simple).
Other topics covered
Non-Normal data - transformations and non-parametric tests. If time is available, we will briefly discuss sample size and power.
You will need to bring your own laptop to the workshop.
Course fees include morning tea and lunch.
You will receive a certificate of completion for the course.
 
 
FAQs
 
How can I contact the organiser with any questions?
Please send us an email at: stats.central@unsw.edu.au

UNSW Business School (E12), Room 119

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