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Simple statistical methods (t-tests, chi-square tests, linear models/regression) assume independence of observations and cannot be used when dependence is present in the sample. Mixed models are extensions of linear models to dependent data. Common reasons for dependence are Clustering e.g. multiple patients per hospital, multiple plants per site, multiple measurements on the same individual Ti...

It All Depends… Analysing Dependent Observations - Luz Palacios-Derflingher

(Speaker: Gordana Popovic) Poor statistical practice can distort scientific findings and mislead readers. This presentation reviews common statistical problems that readers, authors and reviewers of journals articles should be aware of when assessing research. These include p-hacking, multiple comparisons issues, underpowered studies, correlation/causation confusion, and inappropriate statistic...

Practical Study Design - Gordana Popovic

But it isn't significant! What to do with large p-values - Peter Humburg

Five Steps Towards Making Your Analyses Transparent and Reproducible (in R) - Daniel Falster

What Can a Caterpillar Teach Us About Good Data Visualisation? - Nancy Briggs

How Can You Get Closer to a RCT When You Can't Do an RCT? - Nick Olsen

How to avoid p-hacking, HARKing and other statistical sins - Gordana Popovic

Bootstrapping - How to use your data to get out of a tight spot - Peter Humburg

Four strategies for dealing with multiple comparisons - Eve Slavich

A Gentle Introduction to Survival Analysis - Mark Donoghoe

Survey and Questionnaire Design: Practical advice for researchers - Nancy Briggs

T-test a special case of regression? Strange but true … - Peter Geelan-Small

Practical Study Design - Gordana Popovic

It all depends: Interaction terms in regression - Eve Slavich

A Practical Guide to Meta-Analysis - Zhixin Liu

Like Cats and Dogs - Why model selection and inference just can’t get along - Peter Humburg

2020 in graphs - David Warton

When Your Research Meets a Global Pandemic - Nancy Briggs

Methods for ranking and quantifying the importance of predictor variables - Eve Slavich

Visualise high dimensional data with the tourr R package - Gordana Popovic

Variable selection: too many variables? what next? - Peter Geelan-Small

Residuals in linear models: More than just what’s left over - Nancy Briggs

Effect Size - p value is not enough, measure of magnitude matters!- Zhixin Liu

Mind your Ps: How to use and interpret p-values - Mark Donoghoe

Now there are two of them! Why you shouldn't dichotomise your variables - Peter Humburg