P value is associated with a test statistic. It is "the probability, if the test statistic really were distributed as it would be under the null hypothesis, of observing a test statistic [as extreme as, or more extreme than] the one actually observed."

The smaller the P value, the more strongly the test rejects the null hypothesis, that is, the hypothesis being tested.

A p-value of .05 or less rejects the null hypothesis "at the 5% level" that is, the statistical assumptions used imply that only 5% of the time would the supposed statistical process produce a finding this extreme if the null hypothesis were true.

5% and 10% are common significance levels to which p-values are compared.

### Terms related to p value:

- Marginal Significance Value
- t-Statistic
- Augmented Dickey-Fuller Test
### Resources on p values:

- Hypothesis Testing Using One-Sample t-Tests
- Hypothesis Testing With Multivariate Regressions Using One-Sample t-Tests
- How to Do a Painless Multivariate Econometrics Project

### Writing a Term Paper? Here are a few starting points for research on p values:

Books on p values:- Introduction to Robust Estimation and Hypothesis Testing
- Statistical Hypothesis Testing: Theory and Methods
- Statistics for Business & Economics

### Journal Articles on p values: