1. What is a P Value?

The p-value is the probability of obtaining test results at least as extreme as the observed results, assuming the null hypothesis is true. It's a crucial concept in statistical hypothesis testing.

2. How P Value is Calculated

The p-value is calculated using the test statistic and degrees of freedom:

Where:

• Test Statistic — Value calculated from your data (t, χ², F, etc.)
• Degrees of Freedom — Depends on sample size and test type

Explanation: The calculation uses the appropriate statistical distribution (t-distribution, chi-square distribution, or F-distribution) to determine the probability.

3. Interpretation of P Values

Details:

• p ≤ 0.05: Often considered statistically significant
• 0.05 < p ≤ 0.10: Marginally significant
• p > 0.10: Not statistically significant

Note: These thresholds are conventional but context-dependent.

4. Using the Calculator

Tips:

• Enter the test statistic value from your analysis
• Provide the correct degrees of freedom
• Select the appropriate test type
• The calculator will return the one-tailed p-value

5. Frequently Asked Questions (FAQ)

Q1: 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 look in both directions. For two-tailed, often double the one-tailed p-value.

Q2: How do I determine degrees of freedom?
A: For t-test: n-1; for chi-square: (rows-1)*(columns-1); for F-test: (n1-1, n2-1).

Q3: What if my p-value is exactly 0.05?
A: This is exactly at the conventional significance threshold. Consider effect size and practical significance.

Q4: Can p-values be greater than 1?
A: No, p-values are probabilities and always between 0 and 1.

Q5: Why is my p-value reported as 0.0000?
A: This means the p-value is very small (<0.0001). Report as "p < 0.0001".