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Table of Contents
1. Neural Data Characteristics
2. Multiple Comparisons Problem
Correction Methods
3. Double Dipping
4. Statistical Power Crisis
5. p-hacking + Garden of Forking Paths
6. Recommended Methods
7. NumPy — Permutation Test
8. Bayesian Perspective
9. Decoding / MVPA Pitfalls
10. Neural Data Replication Movement
11. Common Pitfalls
11.1 p < 0.05 = True
11.2 Pick Neuron Then Test
11.3 Large Sample Needs No Correction
11.4 n = Number of Animals
11.5 High Decoding = Brain Uses It
12. Related Concepts
References