1. What is a P Value?

A 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 key concept in statistical hypothesis testing.

2. How P Values are Calculated

The general formula for p-value calculation is:

Where:

• \( p \) — Probability value (unitless)
• \( data \) — Input data array (unitless)

Explanation: The specific calculation depends on the statistical test being used (t-test, chi-square, ANOVA, etc.).

3. Interpreting P Values

Details: A smaller p-value indicates stronger evidence against the null hypothesis. Common thresholds are 0.05, 0.01, and 0.001.

4. Using the Calculator

Tips: Enter your data as comma-separated values. Select the appropriate statistical test for your analysis. Note that this calculator provides simulated results - for actual research, use statistical software.

5. Frequently Asked Questions (FAQ)

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

Q2: Is p-value the same as significance level?
A: No, the significance level (α) is a threshold you set before testing, while p-value is calculated from the data.

Q3: Can p-value be greater than 1?
A: No, p-values range from 0 to 1 as they represent probabilities.

Q4: 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.

Q5: Why is p-value controversial?
A: P-values are often misinterpreted and can be misleading if not considered with effect sizes and confidence intervals.