A comprehensive guide to statistical hypothesis testing, covering key concepts from basic definitions through advanced topics like effect sizes and power analysis. The article explains how to properly
conduct and interpret statistical tests, while avoiding common pitfalls and misconceptions in data-driven decision making.
Reasons to Read -- Learn:
how to properly conduct and interpret statistical hypothesis tests, including practical guidelines for avoiding common pitfalls like p-hacking and overemphasis on the 0.05 threshold.
how to use multiple statistical measures (p-values, confidence intervals, and effect sizes) to make more informed decisions in A/B testing, clinical trials, and other real-world applications.
how to perform power analysis and sample size calculations, ensuring your statistical tests have sufficient data to detect meaningful effects with 80-90% confidence levels.
14 min readauthor: Ansh David
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