In statistics, it measures the probability of observing data as extreme as your results, assuming the null hypothesis is true.
A small p-value suggests your findings are unlikely under the null hypothesis, offering stronger evidence to reject the null in favor of your research hypothesis.
If p-value is large, your results are more likely under the null hypothesis. This means you should not reject the null, as there’s not enough evidence against it.
P-values are central in hypothesis testing and statistical significance decisions. A common threshold for significance is 0.05, but this can vary with context.
A p-value < 0.05 typically means your result is statistically significant, and you can reject the null hypothesis in favor of the alternative.
A p-value > 0.05 indicates weak evidence against the null hypothesis, so you fail to reject it—this is not proof it is true.
Remember: P-value does NOT measure the size of an effect or prove your hypothesis is correct. It only assesses compatibility with the null hypothesis.
P-values fall between 0 and 1. The closer to 0, the stronger the evidence against the null hypothesis—a tool for guiding research conclusions, not providing proof