This means that alpha level of 1% requires more statistical evidence than 5%.Why is 5% used as significance level so commonly?The smaller the alpha level, the little is the chance to reject True Null hypothesis..And, also the smaller is the chance to reject the False null hypothesis, because of the tiny area..The more you try to avoid Type I Error, the more you are likely to make a Type II Error.A confidence level is 1- α, which means accepting the null hypothesis when it is True.Beta level means the probability of making a Type II Error, i.e., rejecting the Alternate hypothesis when it is True.P-ValueIt is used by all the hypothesis testing to check the strength of the evidence provided by the population in form of data..It is the evidence against the null hypothesis..The value is between 0 and 1:A small p-value ( ≤ 0.05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis..It means that the sample results aren’t consistent with the null hypothesis that is true.A large p-value (> 0.05) indicates weak evidence against the null hypothesis, therefore you fail to reject the null hypothesis..It means that the sample results are consistent with a null hypothesis that is true.If the p-value is less than or equal to the alpha level, we reject the null hypothesis.Thanks for reading!.You can also follow me on Instagram and connect on LinkedIn.Diva Jain | Coder (@diivan009) * Instagram photos and videos205 Followers, 111 Following, 41 Posts — See Instagram photos and videos from Diva Jain | Coder (@diivan009)www.instagram.com. More details