How to avoid the hidden traps of statistical error
In short: Statistical significance is not infallible. This video examines why p-values can fail us and introduces the concept of statistical power to help researchers better detect real effects.
The discussion focuses on the two primary ways statistical testing can go wrong: Type 1 errors, where an effect is falsely detected, and Type 2 errors, where a genuine effect remains undetected. To combat these inaccuracies, the video introduces statistical power, which measures the probability of successfully identifying an effect when one truly exists.
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Sources
- Summary by Intellect · source: Playing with Power: P-Values Pt 3: Crash Course Statistics #23
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