Quality management frequently uses statistical methods to identify the existence of a quality problem and analyze the root cause of the problem. Statistical methods require the collection of numerical data related to a process being studied. The data can then be used to identify trends that may affect quality, such as the rate of variance in the results of a production process. Descriptive or inferential analysis of statistical methods can also provide information about the most likely causes of the problem. Statistical methods also have predictive value because they can identify potential problems before they have a significant impact on quality (Ryan, 3). Some statistical tools include descriptive statistical data such as frequency distributions, histograms, and inferential statistical analysis approaches such as regression. analysis and analysis of variance (ANOVA). Each tool has advantages and disadvantages when it comes to its use. As a result, the use of the statistical tool often depends on the specific quality problem being investigated. Descriptive Statistics Tools and Histograms Descriptive statistics provide a description of the central properties of data obtained from observations. In quality management, core properties can provide basic information regarding the extent of deviation from a desired standard, which is a major advantage of using descriptive statistics. For example, descriptive statistics can provide information on how often the desired tolerance variance is greater than 10%, with less than 10% as the desired standard. In quality management, the most interesting descriptive statistical data are central tendency, dispersion and frequency (Madan, 268). In ad...... middle of paper ......pes of information. At the same time, the drawbacks of different statistical processes suggest that quality managers should use multiple approaches to analyzing data to ensure that their interpretation of the data is correct. Works Cited Christensen, Eldon, Christine Coombes-Betz and Marilyn Stein. The Certified Quality Process Analyst's Handbook. Milwaukee WI: Quality Press, 2007. Lighter, Donald and Douglas Fair. Principles and methods of quality management in health care. Gaithersburg MD: Aspen Publishing, 2000. Madan, Pankaj. Total quality management. Delhi: Krishna House, 2006. Ryan, Thomas. Statistical methods for quality improvement. Hoboken NJ: John Wiley and Sons, 2011. Tari, Juan and Vincente Sabater. "Quality tools and techniques: are they necessary for quality management? International Journal of Production Economics, 92.3 (December 2004): 267-270.