Understanding P-values
Mar 10, 2025
What is a P-value? A p-value is a statistical measure that helps you determine whether the results of your experiment are significant. In other words, it helps you decide whether to reject the null hypothesis.
Key Concepts:
- Null Hypothesis (H0): The hypothesis that there is no effect or no difference.
- Alternative Hypothesis (H1): The hypothesis that there is an effect or a difference.
- Significance Level (α): The threshold at which you reject the null hypothesis, commonly set at 0.05.
Interpreting P-values:
- P-value < α (e.g., 0.05): Reject the null hypothesis. There is evidence to suggest a significant effect or difference.
- P-value ≥ α (e.g., 0.05): Do not reject the null hypothesis. There is not enough evidence to suggest a significant effect or difference.
Steps to Interpret a P-value:
- Determine the Significance Level (α): This is typically set at 0.05.
- Calculate the P-value: This can be done using statistical software or by looking at a p-value table.
- Compare the P-value to α:
- If P-value < α, reject the null hypothesis.
- If P-value ≥ α, do not reject the null hypothesis.
Example: Suppose you are testing a new drug and want to determine if it is more effective than the current standard treatment. Your null hypothesis (H0) is that there is no difference between the two treatments. After conducting your study, you calculate a p-value of 0.03.
- Since 0.03 < 0.05, you reject the null hypothesis and conclude that there is a statistically significant difference between the two treatments.
