Module 08: Hypothesis Testing in Connectomics

🔬 Scientific Hypotheses in Circuit Neuroscience

Hypothesis testing is the foundation of experimental science. In connectomics, hypotheses may concern the structure, function, or variability of brain circuits. These must be formalized in ways that support measurement and comparison.

• What makes a hypothesis testable?
• Generating predictions from data models
• Null vs. alternative hypotheses

📊 Statistical Tools

Proper testing requires selecting appropriate methods based on sample size, distribution, and effect size. Familiarity with statistical tests is key to avoiding false positives or negatives.

• t-tests, ANOVA, and non-parametric alternatives
• Multiple comparisons and correction
• Visualizing distributions and error bars

🔍 Hypothesis-Driven Experiments

Analysis should be guided by the scientific question, not just exploratory metrics. This section emphasizes how to match your analysis pipeline to your hypothesis.

• Operationalizing hypotheses in code
• Power analysis and sample size estimation
• Reporting significance and effect size

🌟 COMPASS Integration

• Knowledge: Hypothesis formulation and statistical testing
• Skills: Experimental design, statistical computation, error estimation
• Character: Scientific integrity, curiosity, patience
• Meta-Learning: Reflecting on analysis limitations and next steps

📚 References & Resources

• Ghasemi & Zahediasl, 2012. Normality tests for statistical analysis: A guide for non-statisticians. Int J Endocrinol Metab.
• Field et al., 2012. Discovering Statistics Using R. Sage.
• Colab: "Statistical Tests in Python with SciPy"
• BossDB Cookbook: Accessing Lower Resolution Data

✅ Assessment

• Identify a testable hypothesis from a neural network dataset
• Use Python to perform a statistical test (e.g. t-test) and interpret the result
• Explain the significance and limitations of the findings