Precision refer to how close our estimate is to the true population characteristics. Usually we would estimate the population parameter to fall within a range based on the sample estimate. For example let us say that from a study of a simple random sample of 50 pf the total 300 employees in a workshop, we find that the average daily production rate per person is 50 pieces of a particular product.
We might then be able to so that the true average daily production of the product would lie anywhere between 40 and 60 of the population of employees in the workshop. In saying this, we offer an interval estimate within which we expect the true population mean production. The narrower this interval, the greater the precision. For instance if we are able to estimate that the population mean would fall anywhere between 45 and 55 prices of production rather than 40 and 60 then we would have more precision. That is we would now estimate with greater exactitude or precision.
Precision is a function of the range of variability in the sampling distribution of the sample mean. That is if we take a number of different samples from a population and take the mean of each of these we will usually find that they are all different.
We might then be able to so that the true average daily production of the product would lie anywhere between 40 and 60 of the population of employees in the workshop. In saying this, we offer an interval estimate within which we expect the true population mean production. The narrower this interval, the greater the precision. For instance if we are able to estimate that the population mean would fall anywhere between 45 and 55 prices of production rather than 40 and 60 then we would have more precision. That is we would now estimate with greater exactitude or precision.
Precision is a function of the range of variability in the sampling distribution of the sample mean. That is if we take a number of different samples from a population and take the mean of each of these we will usually find that they are all different.
