If you don't like the results of a study ... wait for the next study. It appears that the results from studies involving people vary drastically from study to study ... and they do. At one time butter is bad and margarine is good and then, later, margarine is bad and butter is better (not necessarily good). Fats are bad. No, the right fats are good. Olive oils are the right fats. No, more polyunsaturated fats are even better. Why do studies that involve humans vary in their results so much?
There are quite a few reasons why it is difficult to have consistent results from studies of humans. Some are inherent problems. Some are political problems. And a large number of problems arise from the way studies are reported in the media rather the actual study. In other words, a study may be done very well and present results that are interesting but not conclusive -- but some specific parts are taken up by the media as "startling results". What are the problems with studies?
- People are not mice. Many studies on health effects are done with mice, or guinea pigs, or monkeys, or chimpanzees, or some other more easily studied animal. In addition, many studies may make use of one gender but generalize to both genders. It is rather obvious that, for the best results, use of humans of the appropriate category must be used. Why isn't this the case?
Cost. People want to be paid (in money or value) to participate in studies. Animals can be purchased -- and, until some group starts recognizing what is being done to them, can be treated with the least care needed.
Morality. Except in certain situations (such as Nazi Germany where psychopaths had full permission to experiment) it is not acceptable to put people's lives at risk. This is associated with control groups where they are NOT treated the same as the group which is being tested as well as with the groups being treated with undetermined results. - Patience. It takes TIME to determine what long-term results are. Of course, it doesn't take much time for an immediately lethal poison to be known but most substances aren't as immediate in effect. Taking longer periods of time means results are delayed. It also increases the costs of the study.
Two types of studies of this nature are longitudinal and cross-generational. One examines individuals for substantial portions of their lives and the other examines the effects from parent to child to grandchild. Humans have pretty long lives (unless stopped by disease, accident, or violence) and this also leads to use of shorter lived animals as subjects. - Control groups. A control group is simple in concept but much harder to create and use. The idea is that one group has the variation (tries a medicine, eats a food, does an exercise, endures environmental conditions, ...) and the other does not. Comparing the results of the two groups is hoped to be able to isolate the effects of the specific effect being studied.
A huge difficulty is that it is impossible to prevent the confusion of combinations of variables. You are testing item A. It turns out that A does one thing in the presence of items B and C. It does another, different, thing in the presence of items C and D -- and it may even do something else in the presence of B and D. If B, C, and D are all known and defined then useful results may still be obtained -- but often they are not. These combinatorial variables are unknown -- but they can drastically affect the results. Two of these variables involve environment and genetics.
Environmental variables. What is the effect of electricity in a house or city? In modern society, it is impossible to eliminate -- and, if taken to a part of the world where electricity is NOT present, other variables will exist. What is the effect of plastics? What are the effects of pesticides, hormones, or antibiotics in the food? While these can be minimized, they cannot be eliminated. What about specific pollutants in the air? And so forth.
Genetics. Leading up to the next bullet item on correlation versus causation, I once read a statistical study on smoking and cancer rates in various countries of the world. It turned out that countries with the greatest amount of smoking had among the lowest amounts of cancer. The effect of smoking depended upon the population. (It didn't get a lot of media attention since the outcome was not politically popular.) A homogenous (identical in nature) population is needed for studies and sets of identical octuplets are hard to find. - Correlation versus causation. This is understood by scientists doing studies but easily distorted by politicians, business owners, groups, or media people who have a bias towards a particular result. Causation means the variable causes the result. If I hit my toe with a hammer it will be bruised (or broken). This is true if any toe hit by any hammer causes these results. The effects can be changed -- a hammer hitting a toe that is shielded by a steel-toed boot is NOT hurt (but it also means the toe of the foot is not actually hit).
Some people are allergic to monosodium glutamate (MSG). Some are not. So, MSG AND allergic people cause a particular effect but MSG AND non-allergic affect does NOT cause the effect. This is verifiable contributory causation.
Everyone who drinks water will eventually die. Does drinking water cause people to die? No (unless, of course, the water is contaminated), but this is the type of (often statistical) result that biased groups enjoy mis-interpreting and reporting. - Binary Results. People like simple results. They like "yes" or "no". They do not like long combinations of possibilities. So, a report that says "butter is bad" is much easier to distribute than a report that says "butter, in combination with lack of exercise and excessive refined carbohydrates and a genetic tendency towards high cholesterol, can contribute to high blood pressure".
Properly done studies rarely have simple results.
So, in summary, it is difficult to create a useful, consistent study on the effects of anything with people. Even if done properly, it is difficult to give results without also giving all of the controlled variables along with the result.
