THE USES OF STATISTICS
Statistics are everywhere. In textbooks, advertisements, and even daily conversation. Statistics can be illuminating, yet confusing. They can be helpful, yet misleading. They can save the day, or cause a lot of trouble. It is evident that the vast world of statistics is a super. However, how can they be used for good and for evil?
FOR GOOD AND FOR EVIL
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EVIL
There are vast opportunities to use statistics to better the world around us. To start, statistics allow us to quantify our theories. We are able to convert abstract ideas into data, facts, and calculations. Thus, creating a visual to illustrate the probability that what we spectate is true. With a degree of certainty, we can get a glimpse of the future. This element of reasonable and educated prediction can significantly aid decision making.
In addition, statistics help to simplify our lives by condensing an abundance of information. With a less complicated lens, it is easier to distinctify what is usual and unusual, what is common and what is special, and what is likely and what is unlikely.
Finally, statistics can help us to change with the changing world. Where there is curiosity or innovation, there are statistics. If a former truth seems outdated or a new theory fails to be true, statisticians will reevaluate. Thus, continuing the incredible human desire to discover and refine.
On the contrary, statistics have the potential to be used for evil. Although there is reason behind statistical observation, it is important to remember that statistics are just numbers. There is absolutely no amount of calculation that will yield absolute certainty. There is always variation in statistical findings, which is one of the most dangerous misunderstandings. When most people see statistics, they see facts. However, statistics are predictions, not definite truths. In fact, they almost distort reality because of the factual way in which they are perceived.
Similarly, statistics can be distorted through manipulation. The slightest change in design or decision by the statistician can alter the results of the study. For example, if a statistician uses a small sample size in a study, any usual finding will appear much more unusual because of the small amount of subjects.
Another evil that is associated with the use of statistics is that they can be overused and overlooked. Statistics are everywhere, and they contradict each other to the point where they sometimes lose value.
Also, statistics are made up of vast data sets, and condensing can strip that data of it’s value. As studies gain simplicity, they lose accuracy.
The most prominent evil, however, is that statistics can be confusing. Anyone who wonders “How did they prove this?” or “What does this table even mean?” is faced with a lack of knowledge on the statistical process, and therefore is unable to understand what the statistician put so much work into is proving. Again, the gap between the average person and statistics resurfaces.
Thus, it is clear that there is a plethora of good and bad uses of statistics. In order to take advantage of what the field of statistics has to offer, utilize and embrace the good uses and recognize and challgenge the bad.
For example, think about the Good vs Evil Concept in terms of business...
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Company efficiency - A project manager can use statistics to analyze the efficiency of their business’ performance. They can collect data on any aspect of the process, including product output or employee productivity. With this data, they have a means of pinpointing specific areas that may decrease their success. For example, if they observe a decrease in sales, they can work with their sales and marketing departments reevaluate and refine. They may survey a group of consumers and find that there is dissatisfaction with quality of the product, and use this feedback to make the necessary changes. Thus, hopefully maximizing their company efficiency.
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Decision Making - Statistics can paint an educated picture of the future for a given business. They have the ability to make market predictions, or evaluate and prepare for potential challenges. How well did their previous products or services do? Should they produce or offer them again? What are the upcoming trends in the future of their market? Then, they can make more informed and beneficial decisions. Furthermore, these educated decisions will help businesses conserve time and resources. Although their predictions aren’t absolute, statistical analysis can still be an asset to aid crucial choices.
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Customer Satisfaction - Statistical practices can can open the gate to the minds of consumers. Through survey and observation, a business can use statistics to figure out whether customers are satisfied. They can reach the masses, and organize an overload of opinions. For example, who is buying out products? Are they happy? What else do they want?
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Understanding and Confusion - Statistical thinking can be very challenging. In order to benefit, businesses must master the sometimes complex mindset and mechanics of statistics. First off, many businesses forget that statistics are figures of possibility. There is still a chance that your results will not perform how you predict. Furthermore, if this degree of uncertainty is very high, your findings may be essentially pointless for drawing helpful conclusions. In addition, it is essential to remember that observation never determines a cause and effect relationship. If a business wants to attempt to prove cause and effect, they must design, run, and analyze a full experiment.
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The Challenge of Design - Many businesses overreact to flaws in their studies that causes them to make decisions based on false pretenses. For instance, if a company encounters a couple defective products, they might investigate and disprove the entire production process. However, the errors or defects can be attributed to a plethora of other factors. These errors could be attributed to chance and natural variation, which both are not a problem as long as they’re considered. Additionally, it could be due to a small sample size, which would emphasize errors to make them appear more pressing and prominent. They might have taken a frequential approach in their practices, meaning they treated a frequent event or outcome as normal. However, this “normal” could be the issue in disguise. Finally, their experimental design could contain any variation of bias or a confounding variable.
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Outcome Bias - Many businesses have a desired outcome prior to any statistics. For example, if they are using statistics as a marketing strategy, it is necessary that the outcome is a positive representation of whatever they’re trying to highlight about their business. Therefore, they can alter their statistics to fit their biased outcome. A minor change in sample size, population of interest, or type of test can produce major differences in the results. Plus, these manipulations are almost invisible to the average consumer. Many businesses utilize the factuality associated with a statistic to their advantage. If you saw a statistic that seemed reasonable in an advertisement, would you question it?
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