Precision, Significance Level, Confidence Level, Confidence Interval, Power, Degree of Freedom, p-value,aql, z-value, t-statistics Explained
Precision : In hypothesis testing, precision means how sure we want to be before making a decision. For example, if we want to know if a new medicine is better than an old one, we need to decide how sure we want to be that the new medicine really is better. Do we want to be 90% sure, 95% sure, or 99% sure? The higher the level of precision we choose, the more sure we are before making a decision. It refers to the level of accuracy or the margin of error that you are willing to accept in your estimate or measurement. For example, if you choose a precision level of 5%, it means that you want to be 95% confident that your estimate is within 5% of the true value. In traditional hypothesis testing using the normal distribution, precision is not explicitly used because the significance level is used instead. The significance level determines the probability of rejecting the null hypothesis when it is actually true. It is often set to 0.05, which means that we are willing to accept a 5% pro