Reading P Values and Confidence Intervals in Data Analyst
Many beginners try to jump directly to tools, but strong understanding starts with the basic idea behind the technique.
Chapter Overview
Once results are available, analysts often review p-values and confidence intervals. These terms sound technical, but the core idea is simple: how much confidence do we have that the difference is real?
Interpretation Idea
A low p-value suggests the observed difference is less likely to be random under the null hypothesis. A confidence interval shows a range of plausible effect sizes.
Student Caution
Statistical significance is not the same as business significance. A tiny improvement may be statistically real but too small to matter financially.
Best Practice
Read the statistics together with effect size, sample size, and implementation cost.
Key Takeaways
- Interpret statistical output without overcomplicating the idea.
- This chapter belongs to A/B Testing & Experiment Analysis and is written in a simple student-friendly style.
- Practice with experiment examples to build confidence faster.
