Welcome to clinical data management program using SAS In this video, we will be discussing about the concept of interval estimation and confidence intervals. So, let me explain you all this concept using an example say let x one x two x two dot extent be a random sample from a distribution with a parameter theta that has to be estimated and suppose that we have observed that CDs x one x two x two X and these are all capital X. So, now, they are observed that capital X one is equal to small x one capital X equals to small x two dot capital X equals to small exit now, the theta which is our parameter which has to be estimated let the point estimate of the theater be PETA hat but the point estimate theta hat which is which we are using to estimate theta does not give enough information about the parameter theta.

So, we need some additional information. So, in particular without additional information we do not know how close he died. That is the point estimate is Close to the real value theater. So, here we will introduce the concept of interval estimation. In this approach instead of giving just one value eater as estimate for PETA, we will produce an interval that is likely to include the true value of EDA. That is, we will produce two estimates for theater a high estimate under low estimate in this in interval estimation, there are two important concepts.

One is the length of the reported interval. That is the difference between the high estimate and the low estimate. The length of the interval shows the precision with which we can estimate theta. The smaller is the interval The higher the precision with which we can estimate teacher. The second important factor is the concept of the confidence interval that shows how confident we are about the interval. The confidence level is the probability that the interval that we construct includes the real value of kita therefore, high confidence levels are more desirable.

So, what is inter interval estimation? interval estimation is the use of sample data To calculate an interval of possible values of an unknown population parameter using interval estimation, we make statements that the true parameter lies within some region with some prescribed probability depending on the point estimate. The concept of confidence interval is very much associated with the interval estimation when you say the confidence interval is 95%. This statement means that the probability of the true value of the population parameter lies in that particular interval is 0.95 or 95%. Therefore, higher as the confidence intervals more desert desirable that is tied in with the probability or the true value of the population parameter lies within that particular interval. So, this is your concept of interval estimation and confidence intervals.

In my next video, I will be discussing about how to interpret the statistical measures using univariate procedures in SAS. For now, let me end this video over here. Thank you. Goodbye. see you for the next video.