To understand p-values in hypothesis testing, here are some easy-to-follow points:

1. What is a p-value?
   A p-value tells us how likely we are to get our results, or even more extreme results, if the null hypothesis is true. The null hypothesis is basically the idea that there's no effect or difference.

2. What does it mean?
   We often use certain cutoff points for p-values. These are called significance levels, like 0.05, 0.01, and 0.10. If our p-value is lower than one of these numbers, we can reject the null hypothesis. This means we think there is a significant effect or difference.

3. Looking at the bigger picture:
   It's important to look at p-values in relation to the research you're doing. A small p-value (like less than 0.01) often means we have strong evidence against the null hypothesis.

4. Understanding the importance:
   Remember, p-values don’t tell us how big or meaningful an effect is. To get a better idea of the results, we should also look at confidence intervals.

By following these simple points, you can better understand what p-values mean in research!