Experimental Design and Statistical Methodology
Focuses on experimental design and statistical methodology in experimental neuroscience, covering scientific method, robust approaches, data interpretation, and transparent reporting.
Summary
What you’ll be able to do
Identify appropriate experimental designs relevant to contemporary neuroscience research
Recognize well-designed, well-controlled experiments
Consider experimental design and analysis principles in their own research
Describe appropriate quantitative approaches used in a variety of experimental systems
Recognize (in)appropriate uses of statistics in neuroscience data analysis, interpreting results, and forming appropriate conclusions
Select appropriate application of statistics to neuroscience data in different experimental paradigms
Source: UW Guide
Grade history
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Prerequisites
Course mapGraduate/professional standing
- Graduate/professional standing
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Prerequisite text tree
- Graduate/professional standing
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
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Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|
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Meeting source records
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Student experience
the class
Application of the scientific method and experimental design, with a focus on experimental neuroscience. Topics include best practices that underlie robust and unbiased experimental approaches, methods, analyses, data interpretation and transparent reporting of results.
difficulty & workload
No workload feedback recorded.
Topics
- Scientific method and experimental design in neuroscience
- Methods, analyses, data interpretation, and transparent reporting
- Quantitative approaches in experimental systems
Skills
- Identifying appropriate experimental designs for neuroscience research
- Selecting appropriate statistical applications for neuroscience data
- Applying best practices for robust, unbiased experimental approaches and transparent reporting
Grades
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Sources & history
Catalog & offerings
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Raw records
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Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "8ccb270a06d0683a153f38121f16f5a034e232fe8861b407145e19fd29dc7cfd",
"requirements_status": "valid",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}