A practical introduction to statistical analysis for researchers working with biological and experimental data.

This course teaches how to choose, run, and interpret common statistical analyses using R or Python. The focus is on practical application rather than statistical theory, with hands-on exercises throughout.

Topics include hypothesis testing, ANOVA, regression, linear models, model selection, and power analysis.

By the end of the course, participants should be able to:

• analyse datasets using standard statistical techniques
• select appropriate statistical tests for different research questions
• understand the assumptions and limitations of common methods
• carry out statistical analyses confidently in R or Python

Teaching is primarily hands-on, with short lectures introducing key concepts and methods.

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== Intended audience ==

This course is suitable for:

• researchers and students working with biological or experimental data
• participants who already have basic familiarity with either R or Python
• anyone looking for a practical foundation in statistical analysis

== Course fees ==

All fees are per full training day.

{| class="wikitable"
|-
! Category
! Fee
|-
| Industry full charge
| £130.00
|-
| Academic / Government / charity concessionary
| £65.00
|-
| Cambridge University staff members / postdocs / visitors
| £65.00
|-
| Cambridge University registered students
| Free
|-
| Cambridge University registered students non-attendance
| £22.00
|-
| Special events
| Per event
|}

Payment options will be provided in booking confirmation emails sent after registration.

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== General information ==

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