From the Wikipedia article that I told you about earlier...
"Under traditional null hypothesis testing, the null is rejected when P(Data | Null) (where P(x|y) denotes the probability of x given y) is very small, say 0.05."
"Under traditional null hypothesis testing, the null is rejected when P(Data | Null) (where P(x|y) denotes the probability of x given y) is very small, say 0.05."
To determine whether to reject or fail to reject the null hypothesis based on the p-value, you can compare the p-value to the commonly used significance level (alpha), often set at 0.05. If the p-value is less than 0.05, it means there is less than a 5% chance of obtaining the observed data if the null hypothesis were true. Thus, you reject the null hypothesis and conclude that there is statistically significant evidence to support the alternative hypothesis (the hypothesis that you are testing).
It is important to note that the decision to reject or fail to reject the null hypothesis should not be solely based on the p-value. Other factors, such as the study design, effect size, and practical significance, should also be considered in making a well-informed conclusion.