QRB 501 WEEK 4 STANDARD DEVIATION

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QRB 501 week 4 Standard Deviation,
Standard Deviation
Introduction
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