Recent headlines proclaiming that “vaccines cause autism” or studies claiming a link between vaccines and autism have sparked debates across America.
Many people are skeptical about the safety of vaccines, making it hard to persuade others why they should be given.
Some believe that injecting harmful chemicals into our children is not only unnecessary but could actually do more harm than good. Others worry that even if there was a slight risk of vaccine injuries, it would still be worth it to protect their child from disease.
It can be tricky to determine whether an individual study is biased or accurate. Biased research may suggest a correlation where none exists while inaccurate studies fail to find one. This influences how much confidence you have in the results of a study and what conclusions you reach.
In this article, we will talk about some factors related to scientific studies and how well they represent the truth. We will also look at some ways you can use these tools to evaluate the accuracy of a study.
They are very accurate
Recent studies show that most studies are not just accurate, but also reliable! It has been shown time and again that when researchers do their job correctly, they spend lots of time confirming your current theories.
Studies with null findings don’t prove anything one way or another. But they help us put our current theories to rest because they disprove them!
And even if a study does find an effect, this doesn’t mean that the thing being studied is good for you. A finding is always relative to what we already know, and how people respond to things varies from person to person.
It’s important to remember that even positive studies may be misleading because of statistical flukes- they luck into detecting an effect by chance alone.
By relying too much on studying random pieces of data, science can become overly dependent on having big surveys and experiments run by professionals who have a bias towards the conclusion they want to reach.
They aren’t always accurate
A lot of people have misconceptions about scientific studies- you more likely to find a study that seems supportive than one that is contrary, even when the latter are probably more true.
Studies with positive results get published and spread, while those with negative findings usually don’t. This can create an illusion of accuracy – but it misses the important thing: whether or not a study finds a correlation does not prove cause and effect.
There could be so many other reasons for the result beyond exposure actually causing effects. Or there could be confounding factors — things that influence both exposure and outcome that we never properly account for.
Furthermore, most observational studies suffer from “selection bias” where only individuals with strong beliefs will agree to participate in the research, leading to inaccurate conclusions.
Intervention studies look at whether or not a particular treatment works under ideal conditions, but this doesn’t tell us much about how well it would work in practice.
It depends on the study
Recent criticisms of scientific research are too often influenced by past studies that researchers or others dislike. For example, some people may have pre-existing beliefs about nutrition or diet fads that influence how they evaluate new studies.
There’s also an assumption in these critiques that because a study was published in a peer-reviewed journal that it must be accurate. But this isn’t always the case!
Many well-meaning individuals and organizations publish important health messages in academic journals to promote their own agenda. Sometimes these agendas include diets that pay big money for advertisements or medicine cabinets full of expensive supplements.
It is very difficult to assess the accuracy of individual studies due to all of the factors mentioned above. That is why we use systematic reviews to combine the results of different studies to get a better picture of what works and doesn’t work.
Systematic reviews don’t choose which studies to include based on whether they agree with the reviewer, but on whether they can determine if one group of studies consistently shows effects of the intervention or not. When done properly, this can reduce bias.
Science is always evolving
Even studies that are considered scientific are not constant, therefore making any one study’s findings seem more definitive than they actually are. Because of this, you cannot assume that a study’s conclusions tell us the truth about the effects of certain products or practices.
This is particularly important when it comes to health supplements as most people believe them to be totally safe. Unfortunately, even well-intentioned efforts to improve overall health can have harmful long term consequences.
It is extremely difficult to ascertain whether a supplement is truly effective until we have conducted an adequate amount of clinical trials. This is why it is so crucial for individuals to understand how research is carried out.
There are several reasons why scientific studies often contradict each other, which will be discussed later in this article. However, what almost everyone can agree on is that there is just no way to know if a specific intervention has all of the benefits its proponents claim it does unless these interventions have been properly tested.
Fortunately, there are ways to evaluate the accuracy of purported nutritional therapies by analyzing their effectiveness through randomized controlled trials (RCTs). These tests compare a group of patients given the new treatment to a similar sized control group who did not receive the therapy under investigation.
The effect being studied is typically determined before the test subjects begin the treatments and then assessed at a different time after the experimenters determine appropriate markers.
There are many flaws in scientific studies
Many people use scientific studies as proof of how much impact certain behaviors have on health. However, there are several major problems with relying heavily on research findings.
First, not all studies are conducted under the same conditions or for the same length of time. Some studies may be done only during summer months, for example, which can make it difficult to determine if finding results is due to the exposure or seasonal changes.
Second, some studies do not prove whether or not their findings would apply in the real world. For instance, they may test whether eating chicken pox vaccine will prevent you from getting chickenpox, but they might also test whether eating chicken pox vaccine prevents you from getting other diseases such as measles or herpes.
Third, even if a study does control for those two factors, it may still be invalid because of something unrelated to the outcome that was different between the groups being studied.
For example, one group could have been given special nutritional supplements after the intervention instead of just plain old water, which could potentially affect the result. Or, both groups could have received different levels of stress, which again could influence the outcome.
They aren’t always reliable
Recent studies show that even well conducted research has significant limitations. A recent paper estimated that only about 20% of all studied associations were truly causal.20 Even more concerning, many of these effects disappear when researchers look closer at the data.
A classic example is body mass index (BMI). Most studies determine whether someone is overweight by using either WHO or CDC guidelines for defining obesity.21 Both of those definitions depend on having a large waistline and a high BMI. So if you have a small waist but a normal weight, most studies will consider you obese.
Another problem with relying on observational studies to prove causation is what’s known as confounding. This happens when there are differences between groups being analyzed for an effect. For instance, people who volunteer for health studies may be healthier than average.
Confounding can make it seem like some factor causes your symptoms even though it’s just a coincidence. It also makes it hard to tell which factors are causing the difference in treatment success. This was a major reason why previous trials of diet supplements for depression failed to find any benefit beyond placebo.22
Experimental studies avoid some of these issues because scientists can control for confounding. But they’re not free from problems themselves!
Randomized controlled experiments cost lots of money to conduct and require very strict adherence to ensure fairness for participants and avoidance of contamination during the study. Not every medical intervention meets this standard so we have little information about how other treatments work.
We should always consider studies with caution
Many things can influence how well a study is designed, including what questions it asks, whether it is systematic or random in nature, and even whether it is conducted with adequate resources and at enough depth to be meaningful.
Studies that are too limited in scope may not provide sufficient internal validity (testing whether the results apply beyond the sample used for the research) or external validity (applicability to the population of interest).
Overall, there are many good reasons to believe that most published scientific findings aren’t as strong as they seem.
Indeed, a recent article pointed out that only 2% of medical studies produce statistically significant results.
There are many reasons why studies are inaccurate
One of the biggest issues is how researchers study scientific questions. Most science comes down to observing things and testing hypotheses, so studying anything about health requires gathering information from past experiments as well as new research.
Traditionally, this means researching studies that were done before!
Researchers look at the results of previous studies to determine what seems to work and doesn’t work in terms of disease treatment or prevention. Because scientists want to use the most up-to-date evidence, they have to go through all of the other studies multiple times to make sure their interpretation is correct.
This process is very time consuming and expensive, which may influence who pays for research and what gets funded. It also creates an environment where only studies that seem to show what you want to see get published and spread far and wide.
It’s important to remember that even positive studies could be wrong if they didn’t take special care to rule out confounding factors like chance, sample size, and statistical significance.
