People who answer questions on Quora span a broad range in their beliefs, attitudes, and perspectives. Unlike the mainstream media that skews information to cater to specific demographics, Quora is free of systemic bias. That is, the information on Quora is not skewed toward a particular point of view although individual posts may be significantly flawed.
However, the downside of the information on Quora is that people without any expertise in an area can post on Quora. While some who post on Quora are very intelligent and have expertise to share, others have nothing but emotionally-based opinions. Comments on my posts at the Intelligence-and-iq Quora site often come from people who have significant expertise in an area, and they often enhance the information that I have presented or they add insights that I have missed. However, I have also seen comments on Quora that remind me that there is a lunatic fringe that we normally don’t encounter in our daily circles of friends and associates.
Consequently, if you visit a Quora site, review the credentials of the person who created the post. To check facts or data, you should access AI sources like ChatGPT and Microsoft’s Copilot to check the answers or comments you see on Quora. Research journals and textbooks, especially in the hard sciences are reliable sources for checking answers. In the softer areas of knowledge, where political opinion and ideology slant the research, you must be more wary. For example, research in the nurture vs. nature debate in the second half of the 20th century was strongly skewed by ideology. If you’re statistically literate and you doubt the results of a research paper, access the section of that paper that looks at the methodology, then check the statistical techniques that were used to crunch the data and draw inferences. Those two sections of the paper are the areas where most errors occur and result in faulty conclusions.
However, there are also questions that are not easy to resolve, no matter what sources you use. For example, the relationship between the consumption of wine and potential health benefits was investigated in 1979 by St. Leger et al., who discovered that among a sample of affluent nations, France had the lowest rate of cardiac mortality in spite of the high-fat French diet. Their research asserted:
The principal finding is a strong and specific negative association [correlation] between [coronary] heart-disease deaths and alcohol consumption. This is shown to be wholly attributable to wine consumption.
This conundrum, known as the French paradox, was catapulted into prominence in 1994 with a publication titled, Does Diet or Alcohol Explain the French paradox?The prospect that something as pleasurable as a glass of wine a day could result in health benefits naturally caught widespread headlines. An article in The New York Times on December 28, 1994 titled, “Wine for the Heart: Over All, Risks May Outweigh Benefits,” summarized the study results just in time for New Year celebrations:
Data from developed nations over a period spanning 1965 to 1988, suggested that drinking wine on a regular basis reduces the risk of heart attacks, but it cautioned that the benefits apply only to moderate consumption.
What does all this research tell us? Very little. Drinking some wine is beneficial and drinking more than amount X is harmful, but if we don’t know the value of X, the information is not very helpful. Since X varies significantly across individuals, on account of genes, body mass, general health etc., there are too many variables to specify a reasonable estimate of X for an individual. Recent research suggests that X is much smaller than the earlier research suggested. The next study may increase X.
When confronted with conflicting research, ask yourself, “What is the basis of the research assertions?” If the assertions were based on quantitative measures, then proceed as described above for examining the research papers. Alternatively, ask yourself, “How measurable is this?” In many cases, you will be able to dismiss the research assertion because the question addressed by the research is not amenable to precise measurement.
