What is P Value?
The pvalue is used in hypothesis testing to determine whether to accept or reject the null hypothesis. It is the smallest level of significance where the null hypothesis can be rejected.
The pvalue reflects the strength of the evidence against the null hypothesis. A smaller pvalue means that there is stronger evidence in favor of the alternative hypothesis.
PValue Table and Significance
Statisticians and analysts may use the pvalue to measure the strength of the significant difference, and thus find out if the null hypothesis may be rejected.
(Note: The pvalue is a probability. Computing the pvalue and its cumulative distribution function is almost always done with statistical software.)
The appropriate level of significance is chosen by the hypothesis tester, and then compared to the pvalue. If the pvalue is smaller than the level of significance, then there is more evidence in favor of the alternative hypothesis.
Take a look at the table below to see a quick rundown of how to measure pvalue significance:
PValue Table  

For example, let's say 5% (0.05) is considered the level of significance (this is a typical benchmark). A pvalue of 0.0175 would indicate there is a 0.0175 probability  or very small chance  that you'd be wrong to reject the null hypothesis. Put another way, there is more evidence in favor of the alternative hypothesis.
If the hypothesis tester decided that 1% would indicate a level of significance, however, a pvalue of 0.0175 would indicate acceptance of the null hypothesis.
Related Terms to PValue
Altman ZScore (how statistics is used in stock investing)