Statistics are vital in ensuring that people understand a phenomenon that could be difficult to interpret when assessed by mere observation. This makes individuals, scientists and other professionals use the data to make appropriate decisions and policies in line with the data in question. However, different researchers have published statistics that are misleading thus making it for the audience to make meaning out of the information presented. In some instances, the misleading data is made purposeful to influence a preferred direction while in other cases, it is accidental arising from a wrong sample, inaccurate response to questions by respondents during research or data manipulation by statisticians.

Misleading statistic example

Global warming is a concept widely talked about in social, political and economic sectors of any nation. According to(NASA, 2015, p.17), the world experienced the highest temperature in 1998 due to strong El Nino and temperatures rose to 58.3⁰C. In 2912, the mean temperature was 58.2⁰C. Scientists carried out weather research and developed the data below.

Source: National Aeronautics and Space Administration

Other scientists carried out climatic change research from 1900 to 2012 and came up with the data below:

Source: National Aeronautics and Space Administration

The two graphs describe variation in air temperature at different periods. Though the data presented is accurate in explaining the change in temperature, they can be misleading when interpreting the aspect of global warming. The atmospheric temperature is on the rise due to the increasing deforestation, overstocking, chemical use in agriculture and rapid industrialization increase (Penny & Hamill, 2017, p.169). However, though the first data talks of the variance in air temperature, the short period in data collection could not explain the whole concept of global warming since climatic change occurs after  30 years. The second graph shows a gradual increase in air temperature indicating a gradual occurrence of global warming, an aspect that data users cannot get from the first graph. Therefore, the first data is misleading in explaining global warming.

Fake Advertisement

Apple is a widely recognized company that produces electronic devices such as laptops and mobile phones. The above advert means that the other products of the competitor companies are obsolete or defective. The aspect of 0.15% of consumers complaining about the product means that the products are of quality and that 99.75% of the consumers have a positive taste towards the products. This advert is misleading since it tells potential customers that the only products they should prefer are those produced by Apple yet many consumers have complained about costly and sophisticated features of the Apple product.

A consumer who is after purchasing a device should carry out a market analysis to get accurate data from the electronic sellers and suppliers to verify which devices is appropriate based on workability, simplicity, and cost-effectiveness. Also, purchasers should research other information targeting a different sample spread across the globe to get accurate data concerning Apple products and its market size in comparison with other competitor products.

Conclusion

Statistics are essential in establishing accurate data necessary for decision making. The information provided is essential for consumers who need to know the quality of products in the market before purchasing. However, some of the statistics are accurate on some aspects and misleading to others. Also, some of the data presented are false but purposeful presented to lure people into believing in elements that are mostly business related. It is therefore crucial for users of the information to verify the authenticity of the data before making conclusions and decisions from it.

References

National Aeronautics and space administration (NASA) (2015).Retrieved from:                                                    https://www.aaas.org/sites/default/files/15pch09.pdf

Penny, S. G., & Hamill, T. M. (2017). Coupled data assimilation for integrated earth system analysis and prediction. Bulletin of the American Meteorological Society, 98(7), ES169. doi:10.1175/BAMS-D-17-0036.1″>