P-Value from Z-Score Calculator
Calculate the one-tailed and two-tailed P-value from a Z-score.
P-Value (one-tailed): -
P-Value (two-tailed): -
Use this P-Value Calculator, Z-score calculator, statistical significance, hypothesis testing, probability value calculator for quick, clear estimates. Try a tiny example to see the impact of each input.
Q: What is a P-value used for?
A P-value is used in hypothesis testing to help determine the statistical significance of an observed result. It quantifies the probability of observing a test statistic as extreme as, or more extreme than, the one calculated from the sample data, assuming the null hypothesis is true. Researchers use the P-value to decide whether to reject or fail to reject the null hypothesis.
What is a good P-value?
A “good” P-value is typically considered to be less than the predetermined significance level (alpha, often denoted as α). Commonly used significance levels are 0.05 (5%), 0.01 (1%), or 0.10 (10%). If the P-value is less than alpha, the result is considered statistically significant, indicating that there is strong evidence against the null hypothesis.
What is the difference between Z-score and P-value?
A Z-score is a measure of how many standard deviations an element is from the mean. It standardizes a data point, allowing for comparison across different datasets. A P-value, on the other hand, is a probability that indicates the strength of evidence against the null hypothesis, derived from a test statistic like a Z-score or t-score. In essence, a Z-score helps you locate a data point, while a P-value helps you assess the statistical significance of an entire hypothesis test.
How do you calculate P-value from Z-score?
To calculate a P-value from a Z-score, you typically look up the Z-score in a standard normal distribution table (Z-table) or use statistical software. The Z-table provides the probability of a value being less than or greater than the given Z-score. For a one-tailed test, the P-value is the probability directly from the table (or 1 minus that probability). For a two-tailed test, you multiply the one-tailed probability by two.