In statistics, the p-value is a measure of the statistical significance of the results of a statistical test. It represents the probability that the observed results occurred by chance, given a certain hypothesis or null hypothesis.
The null hypothesis is a statement that assumes that there is no relationship between the variables being tested. For example, if you are testing the effectiveness of a new drug, the null hypothesis might be that the drug has no effect on the condition it is intended to treat.
The p-value helps you to determine whether the observed results are strong enough to reject the null hypothesis. If the p-value is low, it means that the observed results are unlikely to have occurred by chance, and you can reject the null hypothesis in favor of an alternative hypothesis (such as the hypothesis that the drug is effective). On the other hand, if the p-value is high, it means that the observed results are more likely to be due to chance, and you cannot reject the null hypothesis.
In general, a p-value of less than 0.05 is considered to be statistically significant, which means that the observed results are unlikely to have occurred by chance and you can reject the null hypothesis. However, the specific p-value threshold for statistical significance may vary depending on the research context and the goals of the study. It's important to consider the p-value in the context of the research question and the research design, and to carefully interpret the results of any statistical tests you perform.