Confidence Intervals And Statistical Guidelines

Question: Discuss about the Confidence Intervals And Statistical Guidelines? Answer: Guideline 1: The aim of this guideline is to denote the assumptions with different colors. For example, red is the color, which represents the managers knowledge, green signifying the assumption depending on the knowledge and blue is denoting the knowledge from the assumption (Douglas et al., 2015). The guideline is helping the managers to identify that whether their statement is supportive in terms of their knowledge and final assumption or not. In case of a doctor, he or she needs to be more careful while monitoring the patient. Guideline 2: This guideline has suggested that calculating the mean or average value of a statistical calculation and judging the overall statistic with that value is actually represent a false statement. Therefore, it is important to communicate with the service users of healthcare directly and individually for identifying their current satisfaction level. Guideline 3: This guideline is suggesting that predicting the consequences is not appropriate to evaluate a statistic. However, the consequences can be far different from the assumed thought. Doctors are handling health care service users and therefore, the process is very delicate (Altman et al., 2013). Thus, a doctor should take care of the alternative consequences while handling their patients. Guideline 4: Statistics or statistical calculation is not enough to provide a clear outcome. That means an overall analysis of many viewpoints can represent a manipulative result. In order to improve the quality of statistical result, the sampling process needs to be monitored. Doctors need to communicate with every patient for knowing that whether they are improving with the current medication or not. Guideline 5: The result of a statistical calculation is might not the actual result. As authenticity of statistical data is low, it is obvious that the result will be on negative degree from the actual result. Therefore, an individual needs to assume the ultimate outcome lower than the result of a statistical calculation. Doctors should not take their decision depending on the statistical result due to low reliability issue. Summarizing the responses The research conducted in this case study is emphasizing that sometimes statistics are misleading people. Many organizations are conducting study with random sampling and the result is published through different platform such as television, newspaper and internet. However, these studies are actually providing wrong information to the audience. Mostly, companies are involved in these kinds of activities for increasing their sales volume. The case study is showing that a Random Sportswear TM has was conducted a study to increase sales of their new product, which was a running shoe (Griggs et al., 2008). The aim of the company was to portrait that this product is able to improve growth of muscle at a percentage of 15% and 25% accordingly. Comparing these products with the other random shoes, the company has claimed that the experts has tested this product and certify the same. Apart from that, the company was stated that they has selected 100 people and separate those people into two groups for conducting the study. However, they have not provided any information regarding how they have separated the group of how they have determined the parameters to compare the product with random shoes. The study was also overlooked the individual result. Another research has focuses on the individual viewpoint, which represents that 85% of respondents have encountered they were not comfortable with the running shoe (Hutton, 2010). However, the company has stated the wrong information to the consumers for enforcing the sales volume within a short time. The overall survey is indicating that the companies are manipulating numerical data for misleading the consumers. Reference list Altman, D., Machin, D., Bryant, T., Gardner, M., (2013).Statistics with confidence: confidence intervals and statistical guidelines. John Wiley Sons Douglas, J. A., Douglas, A., McClelland, R. J., Davies, J. (2015). Understanding student satisfaction and dissatisfaction: an interpretive study in the UK higher education context.Studies in Higher Education,40(2), 329-349 Griggs, R. A., Jackson, S. L.,Marek, P., Christopher, A. N. (2008). Critical thinking in introductory psychology texts and supplements.Teaching of Psychology,25(4), 254-266. Hutton, J. L. (2010). Misleading statistics.Pharmaceutical Medicine,24(3), 145-149.