P-Value Calculator
Calculate the p-value from a z-score to determine statistical significance.
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Table of Contents
What is a P-Value?
A p-value is a statistical measure that helps scientists determine whether their hypotheses are correct. It represents the probability of obtaining results at least as extreme as the observed results, assuming that the null hypothesis is true.
- Lower p-values indicate stronger evidence against the null hypothesis
- Common significance levels are 0.05 (5%) and 0.01 (1%)
- P-values are not the probability that the null hypothesis is true
- They measure the strength of evidence against the null hypothesis
How to Interpret P-Values
Understanding p-value interpretation:
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1p < 0.001: Very strong evidence against the null hypothesis
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20.001 ≤ p < 0.01: Strong evidence against the null hypothesis
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30.01 ≤ p < 0.05: Moderate evidence against the null hypothesis
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4p ≥ 0.05: Insufficient evidence to reject the null hypothesis
Types of Statistical Tests
Different types of statistical tests and their p-value calculations:
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1Two-tailed test:
Tests for differences in either direction. Most common type of test.
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2One-tailed test (right):
Tests for differences in the positive direction only.
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3One-tailed test (left):
Tests for differences in the negative direction only.
Practical Examples
Example 1 Clinical Trial
A new drug is tested against a placebo. The p-value is 0.03.
This p-value indicates moderate evidence that the drug has an effect, as it's less than 0.05 but greater than 0.01.
Example 2 Educational Study
Comparing test scores between two teaching methods. The p-value is 0.001.
This very low p-value provides strong evidence that the teaching methods produce different results.
Example 3 Market Research
Analyzing customer satisfaction scores. The p-value is 0.08.
This p-value suggests insufficient evidence to conclude a significant difference in satisfaction levels.