Evaluation
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QUESTIONS TO ASK
1. Was the study large enough to pass statistical muster?
2. Was it designed well?
3. Did it last long enough?
4. Were there any other possible explanations for the
conclusions of the study or reasons to doubt the findings?
5. Do the conclusions fit with other scientific evidence? If not,
why?
6. Do you have the full picture?
7. Have the findings been checked by other experts?
8. What are the implications of the research? Any potential
problems or applications?
WHAT IS A GOOD STUDY?
In becoming a sciencebased person, I can imagine a process that
involves three tiers. First, you decide that you are going to get
your information from reputable sources like scientific journals
and then decide that any other claims that you find should have a
similar backing. Second, pushing past the veneer of scientific
legitimacy, you decide to look into the claims for yourself. This
involves not only getting your information from sources based on
scientific journal articles, for example, but also going through the
study yourself to determine whether it is a “good” study. Lastly,
after having navigated scientific sources for some time, you are
What is a Good Study?: Guidelines for Evaluating Scientific Studies
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able to evaluate claims base on methodologies and procedures
that you would expect the offered evidence to have if it were
indeed credible. Because most of us are not scientists and find it
hard to invest in the education it would require to reside
comfortably in the third tier, I will try to offer some help with the
second.
If you ever would consider a career as a science writer or science
journalist, there are a few basic techniques that you must master
or at least become proficient at. Among them are learning
statistics and how to interpret them, interviewing scientists to get
the best information, and how to translate sometimes complex
and technical scientific information into something that the lay
audience can digest. Another fundamental skill that you must
wield effectively is being able to confidently answer the question,
“What is a good study?” To this end, what follows are some basic
questions that you should ask yourself when trying to determine
the validity of a scientific study. You would find these kinds of
questions in any introductory level sciencewriting textbook, and
they will become a valuable tool in your skeptical arsenal.
Keep in mind that when you are evaluating a study, the more of
these questions that you can have answered, the better off you
are. However, if you find yourself questioning every single
procedure, method, and ethical choice in a study, this may be a
red flag in itself. As a properly skeptical consumer of scientific
information, a good place to start at is what is called the null
hypothesis. That is to say, assume that a new medical treatment
or physics experiment won’t work. Without being downright
cynical, greet every claim with this assumption. Your new motto
when faced with a claim in a study or elsewhere should be “show
me.”
Is the study large enough to pass statistical muster?
Numbers are very important in this regard. For example, the
number of patients that a study includes in a clinical trial says a
lot about that trial’s “power,” or relative generalizeability (does
the study include enough patients to distinguish between
treatments?, etc.). Taking a more basic approach, if you were to
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read in a study that “the majority of US citizens now reject the
theory of evolution,” you should find out how many people were in
the study. The statistics turn out that if you have less than around
1,024 people for a nationwide study, the margin or error
exponentially increases beyond three percent. In study that
reports a 49/51 split, this could render the claim worthless.
The other side of this question is to determine if the findings of a
study are statistically significant, meaning that there is only an
acceptably small chance that the findings were due to random
chance alone. The value that is typically used in scientific
research is p=0.05. This “pvalue” means that the probability that
the findings of the study are due to chance alone is only 1 in 20,
or 5% (If this seems to low, it should be noted that many fields in
science have much more rigorous standards. Physicists use p
values of p=0.001 to validate their findings. Still, even with the
less rigorous standards, most scientific papers are made to be
replicated, eliminating chance occurrences even further.) When
evaluating a study, pay close attention to this value. As a general
rule, any correlation that has a pvalue of greater than 0.05
(p>0.05) should not be taken as evidence for anything.
Is the study designed well? Could unintentional bias have
affected the results?
This is hard to determine if you are not familiar with a particular
field, but you are still able to ask questions that should help you
sort the bad studies from the good. Was there a systematic design
to the study that remained the same throughout? What were the
specific hypotheses of the study and how did the study test for
them? If it was a clinical trial, who were the patients and how
were they selected?
More generally, was there a control group? Was the sample
population that the study selected representative of the general
population? Was the study as “blinded” as possible, meaning that
no one involved with the study knew which condition was which
and who was involved with it? Were there any conflicts of interest
that should have been disclosed by the researchers? Funding from
a corporation does not automatically mean that the results of a
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study are false, but it is something that absolutely can bias
research.
Did the study last long enough?
This question may not apply to some sciences, but it is especially
important in medicine. For example, if a study claims that a new
treatment put some cancer patients into remission, the study
should also follow those patients for some amount of time
afterwards to see if they stayed in remission. If all of the
participants died two weeks after the study, you may be getting
horrendously skewed conclusions.
Are there any other possible explanations for the findings or
reasons to doubt the conclusions?
Remembering that correlation does not prove causation, how does
the study frame the findings? Is any association statistically
strong? If a causal link is suggested, does the cause indeed
precede the effect? Are the associations that are found consistent
when other methods are used? Did the study look for other
possible explanations, called confounding variables, which could
explain the results? For example, a study that claims reading
science blogs increases the level of scientific literacy may be
leaving out the confounding variable of formal education, which
could be controlling both.
(For medical claims) Does a treatment really work?
Could the patient’s improvements be changes that are occurring in
the normal course of their disease? This is a source of great
confusion for alternative treatment claims like the ones offered by
homeopathic “medicine.” While a patient may feel better after
taking homeopathic medicine, the improvement could indeed have
nothing to do with the treatment and be just the normal ebb and
flow of illness. Taking this into account, most studies have found
that homeopathic “medicine” does not work.
If a treatment is claimed to work, are there any follow up studies
that are needed to confirm that finding? Are the results applicable
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to the general population? All of these questions should be
answered by the study itself.
Do the conclusions fit other scientific evidence?
Are the results of a study consistent with other findings in that
field? If not, why not? Has the study been replicated and
confirmed?
Virtually no one study proves anything. Consistency and the
preponderance of evidence are what point us in the direction of
truth. Of course, the claims of quantum mechanics and other
seemingly impossible notions are bizarre at first, but they are
then supported and backed up by other research. Contrast this
with a pseudoscience like “free energy.” Mountains of evidence
and thermodynamics as a whole will refute a study claiming to
have cracked the free energy code. A study that goes up against
such opposition is not necessarily wrong, but it better offer some
extraordinary evidence to show that it is not.
Do I have the full picture?
How does this research play into the field as a whole? Does the
study leave out some important aspect of the science that would
prove it wrong? Is the study even relevant given other findings?
Like the previous question, it is important to understand how a
finding fits into other research that has been done. Is it in
opposition? Which way is the field moving? Getting the whole
picture is critical if you want to understand the importance of a
study.
Have the findings been checked by other experts?
This is one of the most important questions that you can ask when
looking at a study. Ask yourself: are there experts who disagree
with the claims in a study? Why or why not? Are the researchers
speaking in an area of their own expertise or have they ventured
outside of it? Does the researcher have a good track record when
it comes to findings standing up to scrutiny?
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Most importantly, as one of the safety nets of science, has the
study been through peer review? Is the journal that the study is
published in reputable? A study coming out of an obscure journal
with no peer review, that is to say, no experts to check over the
work of the researchers, is not necessarily wrong but should be
highly suspect.
What now?
When looking at scientific studies you need to ask even more
basic questions than whether or not the study was systematically
designed. Ask common sense questions, like asking if the data
really justify the conclusions. If the researchers have extrapolated
beyond the evidence, it is warranted? Does the researcher frankly
admit any flaws or limitations of the study? Does the researcher
acknowledge that the findings may be tentative and offer
important caveats?
If you can get your hands on a copy of the original study, and not
a press release of the abstract, do it. You may not be able to
evaluate all of the procedures and methods, but a good study will
be written in a way that answers many of these important
questions. Getting good at this kind of evaluation takes practice,
but no one ever said science was easy.