Term: P-Test
Type: Statistical test
Used in: Hypothesis testing, data science, academic research
Purpose: To measure the strength of evidence against a null hypothesis
Definition
The P-Test is a statistical method used to determine the significance of results in hypothesis testing. It calculates a p-value, which represents the probability of observing a result as extreme as the one measured — assuming the null hypothesis is true.
In simpler terms, a low p-value suggests that the observed outcome is unlikely due to chance, and may indicate that the null hypothesis should be rejected.
Key Features
- P-Value Output: Probability between 0 and 1
- Common Thresholds: p < 0.05 = statistically significant
- Used In: A/B testing, scientific studies, business experiments
- Supports: Decision-making in uncertain environments
- Applies To: T-tests, ANOVA, regression, and more
Common Use Cases
- Medical trials testing new treatments
- Website A/B testing
- Academic studies comparing groups
- Analyzing the impact of policy changes
- Determining if observed effects are likely real
Benefits or Advantages
- Provides a numerical way to assess uncertainty
- Helps prevent false conclusions in research
- Standardizes how scientists test significance
- Widely accepted in peer-reviewed studies
Examples or Notable Applications
A p-value of 0.03 indicates a 3% chance the result occurred by random chance — often enough to reject the null. In A/B testing, a p < 0.05 suggests one version performs significantly better. Used with t-tests, chi-square tests, linear regression, etc.
External Links
This post is for educational purposes only and should not substitute formal statistical training or consultation.