One-tailed and two-tailed p-values. When to use them in statistics
🔹 Definition P-value in statistics tells us how extreme our observed data is, assuming the null hypothesis (H₀) is true. The difference between one-tailed and two-tailed p-values depends on what kind of difference or effect we are testing for. 🔸 1. Two-Tailed Test You use a two-tailed test when you’re testing for any significant difference — either greater than or less than a certain value. ✅ Example: Suppose the average blood pressure (BP) in a population is 120 mmHg. You want to test whether a new drug changes BP (it could increase or decrease). Null hypothesis (H₀): μ = 120 Alternative hypothesis (H₁): μ ≠ 120 If your sample mean = 126, and the calculated p-value = 0.04 (two-tailed) , then this means there’s a 4% chance of observing such an extreme difference (either direction) if the true mean were really 120. If α = 0.05, since 0.04 < 0.05 → Reject H₀. Conclusion: The drug significantly changes BP (either up or down). 🔸 2. One-Taile...