Descriptive and Predictive Statistics

Answer

1. Descriptive and Predictive Statistics

   Decision-making is one of the most crucial aspects of organizational management, and it is often dependent on accurate interpretation of valuable information garnered through statistical means. So far, there are three forms of statistical data: descriptive, prescriptive, and predictive. When utilized effectively, these statistics equip organizational leaders with sufficient knowledge to manage the future proactively. The present piece offers a detailed review of the value of descriptive and predictive statistics in organizational procedures, including decision-making and implementation.

   The Goal of Descriptive Statistic

               Statistics is a notoriously complex subject, especially when perceived by laymen, including a vast majority of human resource personnel. Such an overwhelming reality necessitates the use of information that sheds light on the issue at hand in a relevant, yet, understandable manner. Descriptive statistics are used by researchers and analysts to convey fundamental data on a particular subject. Theoretically, this type of statistics simplifies information to keep all stakeholders in the loop while creating a strong foundation for heavy statistics. Pease, Byerly, and Fitzenz (2012) agree that descriptive statistics mark the preliminary stage of measurement as it ensures that all people are at par with regards to the investigation. When reflecting on the case of human resource management, it is fair to assert that productivity is measured by a broad range of variables including turnover, employee development costs, attendance rate, innovation in the workplace, and revenue per employee. Noteworthy is the fact that these variables are descriptive since they offer in-depth knowledge of workplace trends.

               A clear grasp of the mathematical approach of descriptive variable measurement is quite essential at this point. According to Pease, Byerly, and Fitzenz (2012), variables are usually divided into four types including nominal, ordinal, interval, and ratio. Nominal variables lack numerical values, but they separate information into categories. Given the highlighted variables, it would appear fair to define whether the employee turnover was predominantly male or female. This categorization clarifies the gender that retracted from the workforce: noteworthy is the manner in which this nominal variable ‘turnover’ has been used to describe the trend. On the other hand, ordinal variables are placed relative to other variables. A good example is revenue per employee since, by default, this variable describes the revenue made by a worker in relation to the amount generated by his/her colleague. This approach makes it possible to present numerical information in a descriptive manner: most researchers utilize Likert Scales and other similar tools to display this information visually.

   Like ordinal variables, intervals are numerical, yet descriptive. Pease, Byerly, and Fitzenz (2012) comment that this type of data offers meaningful gaps between numbers based on an arbitrary point. Attendance rate is an interval variable which describes employee turnover based on the number of times the top performer attended work against the record of an incompetent member of the team. This variable is also crucial in human resource research as it sheds light on in-depth matters affecting productivity. Meanwhile, ratio data capitalizes on the zero (0) value as an arbitrary point to give meaning to data. Employee revenue can also be measured in terms of ratio in such a manner that an analyst has a clear grasp of the extent to which high performers surpass underperformers.

   Impact of Predictive Data on Organizational Practice and Performance

               Clearly, descriptive data is instrumental in highlighting trends within an organization. Assuming the statistical data offers a glimpse of the continuation of trends in future, organizational leaders would have an easy time setting strategies to mitigate unwarranted scenarios while maximizing on all valuable aspects of the trend. When viewed from this lens, it is fair to establish that information in trends is not only descriptive but also predictive based on the laws of probability and logical reasoning. However, it is not enough to believe that a trend will continue as witnessed due to historic records (Pease, Byerly, & Fitzenz, 2012). Such a bold sentiment is inspired by the belief that correlation does not offer concrete evidence of causality. In her Tedx Talk, Ionica Smeets (2012) urges statisticians and data analysts to reflect on a broad range of variables while applying logical reasoning to gain knowledge of trends. In light of her argument, it seems wise to assert that the knowledge of trends in organizational performance gives managers a golden opportunity to enrich their practice in future by evaluating the information against a broad range of factors.

   Conclusion

               Apparently, statistics is a unique practice in organizational management as it allows leaders to leverage a broad range of information to enhance decision-making. Descriptive and predictive data are both useful in a business setting since they not only show how certain issues occur in the workplace but also offer foundations upon which further meaningful knowledge can be gained to enhance practice. A clear grasp of fundamental types of variables is also useful for analysts seeking to understand various aspects of the issue under investigation.