Via shmula.com Article
What Is a P-Value?
“Statistics is not an easy field, and anyone who’s had to analyze large volumes of data can quickly tell you the same. The problem often boils down to the fact that you simply can’t trust some discrepancies in the data, and you have to know where those differences are coming from, and how to interpret them properly. … TThe p-value is among the most important statistical terms … and it’s critical that everyone using statistical analysis fully understands what it means.
Meaning of the P-value
The p-value is a number between zero and 1. It represents the probability that the groups within your data set came from the same distribution or behave similar to each other.
For example, if you are analyzing the time to commute to work, and you take 2 different routes to work, we can use p-values to determine if one route is statistically different than the other route. We always start out by assuming both routes are not different from one another. Then we calculate the average and standard deviations of both routes, and use those results to calculate the P-value.
If the p-value is closer to 1, then we would conclude that the routes are very similar to each other. If the p-value is closer to zero, then we would conclude that the routes are different statistically.
Since nothing in statistics is purely 100% guaranteed, the p-value represents the chance that we are incorrect, if we conclude the routes are different, when in fact, that only occurred in our sample (but not in reality). This could be due to random chance in how we collected our data. The closer the p-value to zero, the less chance that we will have made a mistake. …
‘If P is low, H0 must go!’ This means that if the p-value is low (less than 0.05), then H0 (the null hypothesis that the groups are equal to each other) must ‘go’. ‘Go’ would mean that we reject the null hypothesis (H0) and conclude that there is a statistical difference between the groups.”