Statistical quality control is important because it uses statistical methods to monitor the quality of a product. This type of auditing maximizes manufacturing productivity and minimizes errors associated with human judgement.
Statistical quality control relies on statistics and data reports to assess quality and is therefore an efficient means to evaluate a manufactured product. Statistical quality control helps maintain the consistency of how a product is made. Statistical quality control methods can include cause-and-effect analysis, check/tally sheets, histograms, Pareto and scatter analyses, data stratification, defect maps, events logs, progress centers and randomization. Graphical charts and graphs, the part of statistical process control that monitors the manufacturing process, help decipher the statistics and data from quality control reports. One important method of statistical quality control is acceptance sampling. In acceptance sampling, a sample of a product is randomly taken to determine whether or not to continue making the product. If the percentage of "good" or acceptable product is higher than "bad" or defective product in the sample, the product is approved as is and manufacturing continues. If not, the product is rejected and production stops. Any evaluation process that involves samples and human judgement usually assumes a margin of error. With statistical quality control, human error is reduced.