Misleading Statistic

Question: Describe about misleading statistic? Answer: Statistics is the process of collecting, organising and representing a large amount of data. A statistic is said to be a misleading statistics if the statistic misuses the data intentionally or by mistake and gives a wrong interpretation of the data. Misleading statistic can be developed in many ways (Pepe et al., 2013). Forming the statistic on own, dealing with falsified findings, by neglecting the baseline of the statistic, incomplete data, fallacious comparisons and misinterpretation of findings can lead to misleading statistic. An example is considered to explain the concept of misleading statistic. A survey was conducted to know the number of crimes in a country and the factors leading to these crimes. The survey must be conducted throughout the country. However, due to lack of time and workers, the survey could not be conducted throughout the country (Rotunda, 2014). Therefore, the surveyor collected samples from few places, estimated the rest of the data, and completed the data sheet of his own. These leads to falsified findings, which eventually lead to the formation of own statistics. The surveyor distributed the survey form in few places and collected the data from the authorities of those places. The authorities of those places did not give the correct information regarding the number of crimes as they neglected the baseline of the statistic. This lead to falsified findings. Analysis was done on these falsified findings to know the crime rate of the country and the factors influencing the crime rates of the country (Rumsey Unger, 2015). Various kinds of analysis were done on this data. Descriptive statistics, correlation, regression and different statistical tests were conducted on this data. These analyses revealed the results of the findings. The analysis of the collected data revealed that Toledo in eastern region of United States had the more rate of crime. However, while collecting the data, it was ignored that Toledo had less population than many other places the United States. The weighted value should have been considered to analyse the data set (Utts, 2014). The authorities to the surveyor could have reported the number of crimes divided by total number of population. However, the wrong reporting of data had led to wrong analysis of the data and the conclusion came out to be wrong. To know about the factors affecting the crimes, it was found most of the crimes happened out of drug addictions. However, this fact is not true as the major reason for criminal activities is unemployment. As it is known that, an idle brain is a devils mind, so more unemployment leads to more number of crimes. It was also seen that most of the people of Toledo was employed and the employment rate of the city was 95%. Therefore, Toledo could not produce more crimes. Moreover, it was recorded that number of drug adductors were very few in Toledo compared to other cities. Therefore, Toledo could not have more activities that are criminal and there was a contradicting result. Thus, the statistic was a misleading statistic and it gave wrong information about the criminal cases of United States. References Pepe, M. S., Janes, H., Kerr, K. F., Psaty, B. M. (2013). Net Reclassification Index: a misleading measure of prediction improvement. Rotunda, R. D. (2014). The Equal-Protection Clause: A Field Day for Misleading Statistics. Rumsey, D. J., Unger, D. (2015).U Can: Statistics For Dummies. John Wiley Sons. Utts, J. (2014).Seeing through statistics. Cengage Learning.