The role of probability is to forecast specific outcomes whether it be with processes or with product development. Statistically, probability in business research works along a confidence interval which is a range of values that has a particular, defined probability of containing some population parameter. For example, it could be a range of values for sales in which there is a 95% probability of containing the customer population mean. The most controllable method of increasing the precision; thus narrowing the confidence interval, is by increasing the sample size (i.e. taking more samples). It represents an interval that is likely (to some confidence level) to contain the true population parameter of interest (Doane & Seward, 2007). It is based on the presumption that, if the procedure were repeated over and over again, the interval would bound the true parameter “confidence level” percent of the time. So if we test a large population whether it be customers or workers, we can be more confident in the probability of a decision.
Doane, D. P., & Seward, L. E. (2007). Applied statistics in business and economics. Boston, MA: McGraw-Hill/Irwin.