Inferential statistics is also a set of methods used to draw conclusions or inferences about characteristics of populations based on data from a sample.
µ – The mean calculated for a population
σ – The standard deviation calculated for a population
Population : A complete set of data “N”
Samples : A subset of data representing the population “n”
We do Sampling all the time. Whenever we execute a project, it has to be managed under many constraints such as time, cost, resources, among others. Thus, it may not be always feasible for the project to study 100% of the population to derive its inferences. For example, if we are improving the quality of ammunition manufactured in an ammunition factory, we may not be able to do quality test of 100% of the products. This is mainly because the product will get destroyed after testing. Thus, sampling is used in these cases where only a sample of products is taken in for quality testing and inferences are made for the population basis the result of this sampling.
Some other examples of sampling include manufacturing of cars in specific lots i.e. a car manufacturing company manufactures its cars in lots. If it is a lot of 400 cars, they will only test 10 – 15 cars and make an inference of whether to accept the lot or reject it.
Sampling helps in managing the project by utilizing lesser resources and is still effective in getting results. Sampling by and large is done by all of the organizations and thus, it is an important topic for our discussion.