Bias and Ethics in Research Statistics
It is very common for bias to infest a study. Researchers have bias that is intentionally or unintentionally added to every report. It is a good feeling to find statistics that prove the point you wanted to make, but people will often exclude the statistics that go against the point. Any research should be based on facts instead of predictions or feelings. Some people are just not capable of being objective instead of letting feeling and emotions sway what is presented.
Assuming a cause-effect relationship is a problem with statistics. Just like this assignment stated, correlation does not equal causation. If I sang a song before I found $20 on the ground walking home, that does not mean I would find $20 on the ground every time I sang the song. People find statistic that make correlations without realizing that there is a third variable that is not added to the equation. I think this statistical bias is very often unintentional. Although there might be a few that would try to trick people with correlation bias, but it would be pretty obvious to any other trained researchers that it was a quasi-experiment or just opinion researching.
Some researchers use data that is not common to the population or environment that they are claiming results. If a study on Eskimos was done on cold tolerance, you could not try to tell people the results are indicative of all people's cold tolerance. Some researchers try to use studies done in Europe on agriculture and try to say the results could apply to American agriculture. Like the correlation issue, I believe most people do these kind of statistical biases unintentionally.
Some researchers do not take into account that humans are not as predictable as they believe. Human nature is so diverse due to genetics and nurturing that it is an enormous task to predict the actions of any person in an open environment. Just because a person does something in a closed environment, that does not mean it will be repeated in a free environment. This makes many human statistical studies subjective in nature. This is unintentional results, but somewhat unavoidable.
researcher’s ethical responsibilities
A researcher has a responsibility to play the devil's advocate against their own theories. It helps their arguments in the long run. Statistical researchers strengthen their arguments the same way “As Seen on TV” salesmen strengthen their sales arguments. A TV salesmen will show that an electronic product works as intended. Although, they also show that is does work after dipped in water, run over by a truck, or thrown off a building. They also show that the competing product does not work as well, or breaks under stress.
For this reason a statistical researcher should talk about competing ideas and show that the theory is bullet-proof to all other considerations. The statistics should be matched with research from opposing views and contradicting theories. If the statistics are stronger on your argument when matched with competition, than it builds your argument that you are more right. If your competition is stronger, than they have the better argument, and if you ignore that you are being unethical in statistics.
The consumer has a responsibility to read and interpret data to a point. Although we have government agencies and third party watch groups for consumer protection from false advertising, bogus claims, and puffery of products. If a company clearly writes such legalese as “this research was paid for by the company selling the product” you should know that these statistical studies may have a higher frequency of bias towards the product. A reader should go into reading research with an open mind to accept the facts, but sometimes we just want to believe something is good or real and we will trick ourselves by ignoring some of the results. Overall, we can't really regulate people to “not be stupid”, so the best we can do is try to protect everyone from trickery.
Reporting international economic statistics
International economic statistics provides a complicated mixture of data. Every country has differing levels of GDP and power in the global market. If a report was given that 90% of coconuts sold in Nigeria are to men, that could be not comparable to a country that is a major producer of coconuts. There could of only been a handful of coconut sales in Nigeria in a year, but in Guam there may have been thousands of sales in a year. It is hard to compare sales statistics of any commodity between countries because of differing sizes and type of markets.
Validity of statistics in a global economic market is only as strong in moderate regulation. Some countries are more free than others, and information is not always regulated or over regulated by the government. Information that comes from Western countries is subjected to federal regulation. If anything is a lie, the researchers are sued for libel or at least exposed. In eastern countries like China, North Korea, and even Japan countries are over regulated. Only good reports are approved, and many bad reports are silenced by strict government regulation. In places like the Middle East and Africa there is no regulation. Reports from these place are not subjected to any scrutiny, so researchers are free to lie about anything.
Different countries have different standards of living, different standards of products, and different laws. Global statistics only tell what the researchers are allowed to research and they only care about proving the results of things that match their standard of living. Statistics about the availability of toilet paper may be a study of the deep valley of the Amazon. This same study would not exist in America, we would be more likely to have statistical studies of WiFi internet shortages in inner cities.
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