P값 계산기
z점수 또는 t점수에서 통계적 유의성 검정
Reject the null hypothesis. There is statistically significant evidence to support the alternative hypothesis.
Common Significance Levels
calculators.p-value.understandingPValues
출처 및 방법론
P-value from test statistic using normal/t distributionStatistical significance testing
How to Use the P-Value Calculator
The p-value calculator determines statistical significance from z-scores, t-scores, chi-square, or F-statistics. Visualize the rejection region on a distribution curve and get plain-English interpretation of your results.
What is a P-Value?
The p-value is the probability of obtaining results at least as extreme as your observed results, assuming the null hypothesis is true. In simpler terms: "What's the chance of seeing this result if nothing special is happening?"
Interpreting P-Values
- p ≤ 0.01: Very strong evidence against null hypothesis
- p ≤ 0.05: Strong evidence against null hypothesis (standard threshold)
- p ≤ 0.10: Weak evidence against null hypothesis
- p > 0.10: Insufficient evidence to reject null hypothesis
One-Tailed vs Two-Tailed Tests
- Two-tailed: Tests if results are different (in either direction)
- One-tailed (left): Tests if results are less than expected
- One-tailed (right): Tests if results are greater than expected
Types of Test Statistics
- Z-score: For large samples (n > 30) with known population standard deviation
- T-score: For small samples or unknown population standard deviation
- Chi-square: For categorical data and goodness-of-fit tests
- F-statistic: For comparing variances (ANOVA)
Common Misconceptions
- P-value is NOT the probability that the null hypothesis is true
- A small p-value doesn't prove practical significance
- Statistical significance doesn't mean important or meaningful
Related Calculators
For confidence intervals, use our confidence interval calculator. For standard deviation, try our standard deviation calculator.
자주 묻는 질문
A p-value of 0.05 means theres a 5% chance of seeing results this extreme if the null hypothesis is true (i.e., if theres no real effect). Its NOT the probability that the null hypothesis is true. At p < 0.05, we typically reject the null hypothesis.
Use two-tailed when testing for any difference (higher or lower). Use one-tailed when you have a specific directional hypothesis before collecting data. Example: "Drug improves outcomes" (one-tailed) vs "Drug has some effect" (two-tailed). One-tailed tests have more power but only in one direction.
Use z-test when population standard deviation is known or sample is large (n>30). Use t-test when population SD is unknown and sample is small. The t-distribution has heavier tails, accounting for uncertainty in the estimated SD. As sample size grows, t approaches z.
No. A small p-value only indicates statistical significance - that the result is unlikely under the null hypothesis. It doesnt prove your alternative hypothesis, establish causation, or indicate practical importance. Effect size and context matter too.
The 0.05 threshold is a convention, not a mathematical law. R.A. Fisher suggested it in 1925 as a convenient benchmark. Many fields now use stricter thresholds (0.01 or 0.001). The right threshold depends on consequences of errors and field norms.
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