Critical Thinking on Statistical Fallacies and Misinterpretations - Essay Sample

Statistics are essential for providing trends and patterns about insights and events that are happening across the globe. Statistics involves the practice of data collection, analysis, organizing, and presentation of numerical data. For instance, Homeland Security provides statistical reports and readable datasets on immigration, terrorism, and death statistics, among other essential insights. Ideally, most of the world’s knowledge is gathered from statistics. However, there are misleading statistics, which are likely to deceive the receiver, especially if the receiver is not keen enough. Statistics are misleading in a lot of ways, the common ones being misinformation, making of fallacious comparisons and neglecting the baseline, as well as inventing false information. The paper will, however, focus on the statistics provided by the US Department of State Statistics to examine the statistical fallacies and fair judgments based on the presented statistics.

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Statistical Fallacies and Misinterpretations Present in the Data

The main issue concerned with statistics is relevance. For instance, Homeland Security is mainly concerned about the rising tides of terrorism crimes in the US. Still, the data they provide is highly inaccurate since the federal prosecutors often count cases involving marriage frauds and other unrelated crimes as anti-terrorism efforts according to a Washington post. In fact, according to the post, the accuracy of statistics provided by the FBI and the Justice from 2001 to 2005 shows that out of 26 data sets, only two sets of data were found to be accurate (Eggen, 2007). A report on the research concluded that the reporting and the collection of terrorism-related data are haphazard. Such cases have become extreme due to statistical fallacies and misinterpretations.

Some of the common fallacies in statistics include the base rate fallacy and the Simpson’s paradox fallacy. Besides, there are a lot of statistical misinterpretations in statistics. Some of these misinterpretations include misunderstanding the data, the use of incomparable definitions, and deliberately misinterpreting information. All these fallacies and misinterpretations are common in any statistical representation, including statistics by the US Department of State Statistics. More into detail, the base rate fallacy occurs when some critical information is disregarded, especially when making a judgment. In most statistics, people tend to ignore the relative population size when judging events occurring within certain subgroups. For instance, based on the presented statistics by the US Department of States on the number of non-natural deaths, it seems that the total number of non-natural deaths has been disregarded since the total number of deaths seem to be 2312, but from cumulative totals provided the number of deaths seem to 2196, which implies the relative population size of deaths has been disregarded. This case is a perfect case of the base rate fallacy. Another fallacy presented within the data is Simpson’s fallacy, which occurs when trends within certain data changes or reverse when the groups of data are combined. Simpson’s paradox shows a perfect example of misleading statistics since, based on the presented data on the number of deaths caused by accidents was the highest with a cumulative total of 747 deaths. It was followed closely by homicides with a cumulative total of 435 deaths.

Homicides were followed closely by drowning deaths, suicide deaths, and terrorist action with cumulative totals of 303, 258, and 133, respectively. However, when it comes to the top five causes of deaths, the trends seem to have reversed. The difference arises slightly on the suicide, and drowning cases, yet on the presented frequency table, cases of death are high by drowning in comparison to suicide cases. These cases present examples of misleading statistics.

There are also cases of misinterpreting statistics due to accuracy. Generally, it is assumed that statistics are accurate, but their interpretations may not be accurate. The accuracy noted refers to the closeness to the measured value. It is determined by a lot of factors such as the individuals interpreting the data, the way the data is collected, the way interpretations are carried out, and the appropriateness of the data being collected. All these factors determine the accuracy of the data, which explains why the level of significance is needed. For the presented data interpretations by the US Department of Statistics, the interpretations have not been based on the level of significance, which is a cause of concern. In other words, the conclusions on the provided data do not incorporate the level of significance. Despite the above fallacies, the presented data is well organized, and everyone can easily understand what the data presentations mean. More so, the presented data is accompanied by graphics to identify the patterns and the trends of the presented data. The graphical presentations in place include the pie chart, frequency tables, and bar graphs. Each of these presentations is well labeled to understand the statistics. The statistics are presented along with their respective percentages. Therefore, the statistics are well presented, which is a fair judgment on the US Dept. of State Statistics.

Conclusion

In conclusion, statistics are effective in providing knowledge on different patterns and trends. However, there are cases of misleading statistics due to fallacies and misinterpretations. The common fallacies and misinterpretations include the base rate fallacy, Simpson’s paradox, and inappropriate data interpretations. Based on the US Department of State Statistics, Simpson’s paradox and the base rate fallacies are evident in the presented data. The base rate fallacy is present in the data presented because the total number of non-natural deaths is disregarded in the frequency table.

References

Eggen, D. (2007). Justice Dept. Statistics On Terrorism Faulted Most Numbers Inaccurate, Audit Shows. Washington Post. https://www.washingtonpost.com/wp-dyn/content/article/2007/02/20/AR2007022001566_pf.html