Home
Back
P-value Calculation:
| From: | To: |
The p-value calculation determines the probability of obtaining test results at least as extreme as the observed results, assuming the null hypothesis is true. It's a fundamental concept in statistical hypothesis testing.
The calculator uses the formula:
Where:
Explanation: The calculation involves finding the area under the t-distribution curve beyond the observed t-value, representing the probability of obtaining such an extreme result by chance alone.
Details: P-values are crucial for determining statistical significance in research studies, helping researchers make informed decisions about rejecting or failing to reject null hypotheses.
Tips: Enter the t-statistic value and degrees of freedom. Ensure values are valid (df > 0). The calculator will compute the corresponding p-value.
Q1: What is a statistically significant p-value?
A: Typically, p-values less than 0.05 are considered statistically significant, though this threshold may vary depending on the field of study.
Q2: How does degrees of freedom affect the p-value?
A: Higher degrees of freedom make the t-distribution approach normal distribution, affecting the critical values and consequently the p-value calculation.
Q3: When should I use one-tailed vs two-tailed p-values?
A: One-tailed tests are used when the direction of the effect is specified in the hypothesis, while two-tailed tests are used when any difference from the null is of interest.
Q4: Are there limitations to p-value interpretation?
A: Yes, p-values don't measure effect size or practical significance, and can be influenced by sample size. They should be interpreted in context with other statistical measures.
Q5: Can p-values be exactly zero?
A: In practice, p-values are never exactly zero but can be extremely small (e.g., < 0.0001), often reported as p < 0.0001 in scientific literature.