Biases occur when researchers subconsciously or consciously favor one theory over another for any reason, whether conscious or not.
Biased research has significant implications both for individual theories of disease and for scientific studies as a whole.
It can skew our understanding of how diseases work, what is and isn’t effective treatment, and which interventions are worth investing money into.
By now you’ve probably seen some headlines about a certain diet being “scientifically proven” to prevent and treat cancer.
But before rushing out and starting that plan, there’s an important thing to know about this particular diet. And it’s something that very few people have actually spoken about!
We’re going to be talking about it here today – just like we talk about sugar and obesity problems, prejudice and discrimination issues, or why using condoms is so crucial to HIV prevention. It’s a topic that too many people ignore, but should be addressed more thoroughly.
Look at the sample size
A large sample size is an important factor in determining whether there was bias in research studies. If a study has a very small sample size, then it may not be representative of the population it aims to represent.
Studies with larger sample sizes are more likely to produce accurate results because they contain enough data to make conclusions. This also means that researchers have enough samples to detect differences between groups when biases exist.
For example, let’s say you want to know how well computer games help people learn. So, you choose 10 popular video games and ask 50 different individuals to test them out for an hour a week for a month.
After the 30-day trial period, you compare the performance of those who completed the program to those who did not and see what factors influenced success or failure.
This isn’t perfect, but it will give you some good insights. The problem is, most game companies don’t agree to do this kind of research, so it becomes difficult to conduct such experiments.
Another reason why studies can look biased is due to funding. For instance, pharmaceutical companies typically spend lots of money encouraging doctors to prescribe their drugs instead of others. This is a form of bias since doctors are paid by the company to recommend their products.
Look at the time period
Recent studies suggest that we are living longer than ever before due to advances in medicine and technology, as well as improved nutrition and health habits. This increase in average lifespan is particularly significant because it has occurred during an era when people lived much shorter lives than they do today.
There’s been a growing concern about whether this extended life expectancy is worth it. Some argue that all of these new technologies and treatments may be prolonging suffering for some patients with serious or chronic conditions. They say that even though you might live a little bit longer, your quality of life likely will not!
Other critics question the ethics of using expensive therapies and tests that can cost tens or hundreds of thousands of dollars per patient to promote research and development.
We also need to consider how widespread many of these practices become — something that can have major implications if they don’t work or prove safe. For example, although there is no evidence that show that nutritional supplements like vitamin D reduce risk of disease, many individuals use them so heavily that they must be working for most people.
Look at the location
Recent studies show that where you do your research can influence what conclusions you reach about the effects of treatments. For example, if most of your research is done at an academic medical center, you may give more weight to studies conducted there than individuals doing research outside the setting.
The same thing happens when researchers are paid for their work- those with an incentive to see a product succeed will likely promote it more heavily. This effect is called study bias.
It’s important to be aware of such biases because we as consumers pay attention to advertising, so why should we believe anything else about a treatment we aren’t being actively marketed to?
By using our knowledge of how marketing works, however, we can avoid some of this bias. Because while advertisements tell us something about the company that puts them out, they won’t usually tell us the whole truth.
So before you make any decisions, do some homework by looking into not only the efficacy of the product, but also its risks and who benefits from it.
Look at the population
A major source of bias in research is researchers who are not representative of the overall population. Researchers may subconsciously put more effort into finding results that support their own beliefs or theories, which can influence what they assess as important variables and whether they test these variables at all.
This effect is called systematic bias. Systematic biases occur when there’s an underlying motivation for doing something, such as studying a particular condition or drug.
Because systemic biases exist, it is very difficult to know if any one study is totally free from them. You have to look at the studies together to see if your confidence in any individual findings decreases due to potential biasses in other areas.
Furthermore, you cannot assume that because a study was successful once, it will be the same success every time. An example of this would be assuming that eating only meat dishes twice per week makes you lose weight effectively. Many people use that advice, but it does not work for most individuals.
When looking at research, make sure to evaluate it critically and do not let yourself be influenced by personal experiences or emotions. Use clear thinking to determine how much trust you should give each finding.
Look at the sample
A key component of avoiding bias is ensuring that your samples are not biased. This includes selecting participants or research subjects, gathering appropriate consent, and analyzing the data with an understanding eye.
When studying any topic, it is important to make sure that you have a representative sample. Participants who take part in studies typically do so because they want to learn more about the study’s focus. They may be paid for their time, but only if they participate!
By having a diverse range of participants, different people can draw from past experiences to help them relate to the material. For this reason, making sure there is no discrimination against race, gender, socioeconomic status, or other characteristics is very important when choosing participants.
Another way to look at it is that including all these groups helps ensure that what we know about the effects of scientific interventions is good quality information. While it is natural to feel passionate about some treatments over others, this cannot influence how well they work unless you include those with poor results as well as those which seem effective.
If possible, find ways to objectively measure success, such as using validated questionnaires or performance tests instead of self-report measures like surveys or interviews. You could also ask patients and caregivers directly about their experiences rather than relying on reports given by doctors and therapists.
Look at the data collection methods
There are two main types of research studies that people use to determine how products work or don’t work. One is called an observational study, where researchers look at whether there is a correlation between doing something and getting a result.
For example, they might give some individuals a product for review and see what happens to their skin or if they feel better after using it. Another type is a controlled experiment, where one group is given the product while another is not so we can be sure that any changes seen were because of the drug rather than anything else.
By studying both groups, we can infer whether the drug had an effect independent of whatever factors may have made the initial change.
Look at the data
A major source of bias in research is researchers interpreting results in ways that favor their own hypotheses or theories. This can be due to personal motivation, such as wanting to prove your theory right, or because you want to defend what you believe about how nature works.
It can also be because someone else’s ideas are more interesting than yours, which creates incentive to interpret the findings in a way that makes them look good.
All too often, these biases go unnoticed, even by other scientists who work with the researcher later. If you are ever working on a project related to human health and behavior, make sure to ask if there were any significant limitations to the study, whether they were acknowledged at the time or not.
If possible, try to do some preliminary studies yourself to see if there are similar effects in other settings, or opposite effects. Sometimes things just don’t seem to work for no reason!
By doing this, you will bring down one of the biggest sources of scientific bias.
Look at the conclusions
The vast majority of studies have sound, rigorous methods that are free from bias. Even highly biased research can be meaningful if we look more closely at what the findings mean. For example, many studies suggest that eating healthy makes you feel better. But is it because healthier diets make you feel good or because they actually help your health?
There’s another way to interpret study results. A lot of times researchers will test whether one diet is superior to others before concluding that only one diet is best. This tactic, known as comparative effectiveness research, has been criticized for using false logic.
For instance, a study may conclude that both low-fat and whole food diets are effective, but not tell you which is better. Or it could pick one over the other without justification. So how do you know which approach works for you?
You can learn by reading past studies and experimenting with different foods yourself. Plus, there are now lots of apps and tools designed to help you find the right balance of nutrients for your body. But even those aren’t perfect, so don’t rely too heavily on them!
Running tests on yourself is always the best first step towards improving your own health. Luckily, science does not bar people from trying out new diets for themselves. You deserve credit for your efforts instead of being belittled for choices you made to improve your wellness.