1. What is a P Score?

The P Score (p-value) is a statistical measure that helps determine the significance of results in hypothesis testing. It represents the probability of obtaining results at least as extreme as the observed results, assuming the null hypothesis is true.

2. How P Score is Calculated

The calculation depends on the statistical test being used:

Common methods:

• T-Test: Based on t-distribution
• Chi-Square Test: Based on chi-square distribution
• ANOVA: Based on F-distribution

3. Importance of P Score

Details: The p-value helps researchers determine whether to reject the null hypothesis. A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis.

4. Using the Calculator

Tips: Select the appropriate statistical test, enter the test statistic value, and provide the degrees of freedom. The calculator will estimate the p-value.

5. Frequently Asked Questions (FAQ)

Q1: What does p < 0.05 mean?
A: It means there's less than a 5% probability the results occurred by random chance under the null hypothesis.

Q2: Is a smaller p-value always better?
A: Not necessarily. While smaller p-values indicate stronger evidence against the null, effect size and practical significance are also important.

Q3: What's the difference between one-tailed and two-tailed p-values?
A: One-tailed tests look for an effect in one direction, while two-tailed tests consider both directions.

Q4: Can p-values prove a hypothesis?
A: No, p-values only provide evidence against the null hypothesis. They don't "prove" the alternative.

Q5: What are common p-value thresholds?
A: Common thresholds are 0.05, 0.01, and 0.001, but these are arbitrary conventions.