QRB 501 week 4 Standard Deviation,
Standard deviations (SD) and variance are commonly used statistical tools that measure dispersion, risk, and predict certain outcomes in the business world through data. Through decades of academic research, investors and businesses have settled on standardized norms or patterns for various types of calculations, using standard deviations. Data sets, like mean or median, are manipulated to make an inference. This abstract will highlight five articles where the SD is assimilated in mixt conditions or settings. Each article will classify the purpose, any research questions, hypothesis, and the main findings.
What to Use to Express the Variability of Data: Standard Deviation or Standard Error of Mean?
Statistics is a major element in any industry, failing to provide adequate information to the reader can easily mislead the receivers. Therefore, using the correct highlights to display the variability of data, can minimize error and clarify any study. According to Barde (2012), “It is depressing to find how much good biological work is in danger of being wasted through incompetent and misleading analysis” (p. 113).
Case in point, this experiment was based on the hypothesis that applying a biomedical research data with Standard Deviation (SD) is more effective than presenting data with Standard Deviation of Mean (SEM). Standard Deviation is used “interchangeably to express the variability; though they measure different parameters. SEM quantifies uncertainty in the estimate of the mean whereas SD indicates a dispersion of the data from mean” (Barde, 2012). The researchers compared experimental results from the cholesterol level of 10 individuals with SD, and the mean of 25 groups of 10 individuals with SEM. As predicted, it was more comprehensible to understand the exact cholesterol level of 10 individuals than the mean of groups to non-biomedical readers. The writer also emphasized how reporting errors can cause major misrepresentations in research when calculating the mean with SEM. Using SEM, finding the average and focusing on the mean of the group creates error and fails to demonstrate the point of the research. Data is essential but can be vague if presented with the use of improper methods. Thus, in any study or the business world representing critical data with improper formulas can cause harmful impacts to the business or research. It is important to eliminate error by using the proper formulas and SD to inform and influence readers at any setting.
Estimating the Standard Deviation for Statistical Process Control
The design of Donald Marquardt’s article is to explain how to determine the standard deviation (SD) for statistical process control. In order to calculate the SD for statistical