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09:30-12:30 (BST) Tuesday 12th May
09:30-12:30 (BST) Wednesday 13th May
09:30-12:30 (BST) Thursday 14th May
From the organizers:
"Join experienced biostatistician Katrina Gore & Dr Nikki Osborne to improve your knowledge & understanding of statistical analysis methods, how to apply them to different experimental design & about data visualisation best practices.
This course involves:
Three 3-hour live & interactive online sessions, plus discussion & practical activities to complete to reinforce learning. Upon completion of the course
participants will receive a certificate of attendance for 9-hours CPD.
09:30-12:30 (BST) Tuesday 12th May - Session 1. Basics of visualisation and hypothesis testing
09:30-12:30 (BST) Wednesday 13th May - Session 2. Estimation & Precision, Significance tests and Basics of ANOVA
09:30-12:30 (BST) Thursday 14th May - Session 3. Extending ANOVA and reporting results.
Session one will cover:
- Understanding, identifying, minimising variation and its impact.
- How the scientific question and experimental design influence the choice of statistical analysis method.
- What summary statistics are available and when to use them.
- Why and how to transform your data, plus strategies for dealing with outliers.
- The structure of a significance test using a two-sample t-test as an example.
Session two will cover:
- What measures of precision should be used and when, including SD Vs SEM plus confidence interval.
- Non-parametric statistical tests and other simple tests to compare more complex data sets.
- Basics of ANOVA and why it should often be used instead of multiple t-tests.
Session three will cover:
- What experimental designs and data features require an extension to statistical analysis methods such as ANOVA and how to incorporate them.
- ANOVA for randomised block designs and factorial treatment structures.
- Analysis of Covariance, ANCOVA Vs Change from Baseline.
- Assumptions of ANOVA and ANCOVA."
For more information and registration, please visit the event page here: Statistical analysis for in vivo and in vitro scientists