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What’s in it for me: Ten critical questions to navigate media coverage of latest scientific findings

research_actionHere at Greater Good, we cov­er research into social and emo­tion­al well-being, and we try to help peo­ple apply find­ings to their per­son­al and pro­fes­sion­al lives. We are well aware that our busi­ness is a tricky one.

Sum­ma­riz­ing sci­en­tif­ic stud­ies and apply­ing them to people’s lives isn’t just dif­fi­cult for the obvi­ous rea­sons, like under­stand­ing and then explain­ing sci­en­tif­ic jar­gon or meth­ods to non-spe­cial­ists. It’s also the case that con­text gets lost when we trans­late find­ings into sto­ries, tips, and tools for a more mean­ing­ful life, espe­cial­ly when we push it all through the nuance-squash­ing machine of the Inter­net. Many peo­ple nev­er read past the head­lines, which intrin­si­cal­ly aim to over­gen­er­al­ize and pro­voke inter­est. Because our arti­cles can nev­er be as com­pre­hen­sive as the orig­i­nal stud­ies, they almost always omit some cru­cial caveats, such as lim­i­ta­tions acknowl­edged by the researchers. To get those, you need access to the stud­ies them­selves.

And it’s very com­mon for find­ings to seem to con­tra­dict each oth­er. For exam­ple, we recent­ly cov­ered an exper­i­ment that sug­gests stress reduces empathy—after hav­ing pre­vi­ous­ly dis­cussed oth­er research sug­gest­ing that stress-prone peo­ple can be more empath­ic. Some read­ers asked: Which one is cor­rect? (You’ll find my answer here.)

But prob­a­bly the most impor­tant miss­ing piece is the future. That may sound like a fun­ny thing to say, but, in fact, a new study is not worth the PDF it’s print­ed on until its find­ings are repli­cat­ed and val­i­dat­ed by oth­er studies—studies that haven’t yet hap­pened. An exper­i­ment is mere­ly inter­est­ing until time and test­ing turns its find­ing into a fact.

Sci­en­tists know this, and they are trained to react very skep­ti­cal­ly to every new paper. They also expect to be greet­ed with skep­ti­cism when they present find­ings. Trust is good, but sci­ence isn’t about trust. It’s about ver­i­fi­ca­tion.

How­ev­er, jour­nal­ists like me, and mem­bers of the gen­er­al pub­lic, are often prone to treat every new study as though it rep­re­sents the last word on the ques­tion addressed. This par­tic­u­lar issue was high­light­ed last week by—wait for it—a new study that tried to repro­duce 100 pri­or psy­cho­log­i­cal stud­ies to see if their find­ings held up. The result of the three-year ini­tia­tive is chill­ing: The team, led by Uni­ver­si­ty of Vir­ginia psy­chol­o­gist Bri­an Nosek, got the same results in only 36 per­cent of the exper­i­ments they repli­cat­ed. This has led to some pre­dictably provoca­tive, over­gen­er­al­iz­ing head­lines imply­ing that we shouldn’t take psy­chol­o­gy seri­ous­ly.

I don’t agree.

Despite all the mis­takes and overblown claims and crit­i­cism and con­tra­dic­tions and arguments—or per­haps because of them—our knowl­edge of human brains and minds has expand­ed dra­mat­i­cal­ly dur­ing the past cen­tu­ry. Psy­chol­o­gy and neu­ro­science have doc­u­ment­ed phe­nom­e­na like cog­ni­tive dis­so­nance, iden­ti­fied many of the brain struc­tures that sup­port our emo­tions, and proved the place­bo effect and oth­er dimen­sions of the mind-body con­nec­tion, among oth­er find­ings that have been test­ed over and over again.

These dis­cov­er­ies have helped us under­stand and treat the true caus­es of many ill­ness­es. I’ve heard it argued that ris­ing rates of diag­noses of men­tal ill­ness con­sti­tute evi­dence that psy­chol­o­gy is fail­ing, but in fact, the oppo­site is true: We’re see­ing more and bet­ter diag­noses of prob­lems that would have com­pelled pre­vi­ous gen­er­a­tions to dis­miss peo­ple as “stu­pid” or “crazy” or “hyper” or “blue.” The impor­tant thing to bear in mind is that it took a very, very long time for sci­ence to come to these insights and treat­ments, fol­low­ing much tri­al and error.

Sci­ence isn’t a faith, but rather a method that takes time to unfold. That’s why it’s equal­ly wrong to uncrit­i­cal­ly embrace every­thing you read, includ­ing what you are read­ing on this page.

Giv­en the com­plex­i­ties and ambi­gu­i­ties of the sci­en­tif­ic endeav­or, is it pos­si­ble for a non-sci­en­tist to strike a bal­ance between whole­sale dis­missal and uncrit­i­cal belief? Are there red flags to look for when you read about a study on a site like Greater Good or in a pop­u­lar self-help book? If you do read one of the actu­al stud­ies, how should you, as a non-sci­en­tist, gauge its cred­i­bil­i­ty?

I drew on my own expe­ri­ence as a sci­ence jour­nal­ist, and sur­veyed my col­leagues here at the UC Berke­ley Greater Good Sci­ence Cen­ter. We came up 10 ques­tions you might ask when you read about the lat­est sci­en­tif­ic find­ings. These are also ques­tions we ask our­selves, before we cov­er a study.

1. Did the study appear in a peer-reviewed journal?

Peer review—submitting papers to oth­er experts for inde­pen­dent review before acceptance—remains one of the best ways we have for ascer­tain­ing the basic seri­ous­ness of the study, and many sci­en­tists describe peer review as a tru­ly hum­bling cru­cible. If a study didn’t go through this process, for what­ev­er rea­son, it should be tak­en with a much big­ger grain of salt.

2. Who was studied, where?

Ani­mal exper­i­ments tell sci­en­tists a lot, but their applic­a­bil­i­ty to our dai­ly human lives will be lim­it­ed. Sim­i­lar­ly, if researchers only stud­ied men, the con­clu­sions might not be rel­e­vant to women, and vice ver­sa.

This was actu­al­ly a huge prob­lem with Nosek’s effort to repli­cate oth­er people’s exper­i­ments. In try­ing to repli­cate one Ger­man study, for exam­ple, they had to use dif­fer­ent maps (ones that would be famil­iar to Uni­ver­si­ty of Vir­ginia stu­dents) and change a scale mea­sur­ing aggres­sion to reflect Amer­i­can norms. This kind of vari­ance could explain the dif­fer­ent results. It may also sug­gest the lim­its of gen­er­al­iz­ing the results from one study to oth­er pop­u­la­tions not includ­ed with­in that study.

As a mat­ter of approach, read­ers must remem­ber that many psy­cho­log­i­cal stud­ies rely on WEIRD (West­ern, edu­cat­ed, indus­tri­al­ized, rich and demo­c­ra­t­ic) sam­ples, main­ly col­lege stu­dents, which cre­ates an in-built bias in the discipline’s con­clu­sions. Does that mean you should dis­miss West­ern psy­chol­o­gy? Of course not. It’s just the equiv­a­lent of a “Cau­tion” or “Yield” sign on the road to under­stand­ing.

3. How big was the sample?

In gen­er­al, the more par­tic­i­pants in a study, the more valid its results. That said, a large sam­ple is some­times impos­si­ble or even unde­sir­able for cer­tain kinds of stud­ies. This is espe­cial­ly true in expen­sive neu­ro­science exper­i­ments involv­ing func­tion­al mag­net­ic res­o­nance imag­ing, or fMRI, scans.

And many mind­ful­ness stud­ies have scanned the brains of peo­ple with many thou­sands of hours of med­i­ta­tion experience—a rel­a­tive­ly small group. Even in those cas­es, how­ev­er, a study that looks at 30 expe­ri­enced med­i­ta­tors is prob­a­bly more sol­id than a sim­i­lar one that scanned the brains of only 15.

4. Did the researchers control for key differences?

Diver­si­ty or gen­der bal­ance aren’t nec­es­sar­i­ly virtues in a research study; it’s actu­al­ly a good thing when a study pop­u­la­tion is as homoge­nous as pos­si­ble, because it allows the researchers to lim­it the num­ber of dif­fer­ences that might affect the result. A good researcher tries to com­pare apples to apples, and con­trol for as many dif­fer­ences as pos­si­ble in her analy­sis.

5. Was there a control group?

One of the first things to look for in method­ol­o­gy is whether the sam­ple is ran­dom­ized and involved a con­trol group; this is espe­cial­ly impor­tant if a study is to sug­gest that a cer­tain vari­able might actu­al­ly cause a spe­cif­ic out­come, rather than just be cor­re­lat­ed with it (see next point).

For exam­ple, were some in the sam­ple ran­dom­ly assigned a spe­cif­ic med­i­ta­tion prac­tice while oth­ers weren’t? If the sam­ple is large enough, ran­dom­ized tri­als can pro­duce sol­id con­clu­sions. But, some­times, a study will not have a con­trol group because it’s eth­i­cal­ly impos­si­ble. (Would peo­ple still divert a trol­ley to kill one per­son in order to save five lives, if their deci­sion killed a real per­son, instead of just being a thought exper­i­ment? We’ll nev­er know for sure!)

The con­clu­sions may still pro­vide some insight, but they need to be kept in per­spec­tive.

6. Did the researchers establish causality, correlation, dependence, or some other kind of relationship?

I often hear “Cor­re­la­tion is not cau­sa­tion” shout­ed as a kind of bat­tle cry, to try to dis­cred­it a study. But correlation—the degree to which two or more mea­sure­ments seem to change at the same time—is impor­tant, and is one step in even­tu­al­ly find­ing causation—that is, estab­lish­ing a change in one vari­able direct­ly trig­gers a change in anoth­er.

The impor­tant thing is to cor­rect­ly iden­ti­fy the rela­tion­ship.

7. Is the journalist, or even the scientist, overstating the result?

Lan­guage that sug­gests a fact is “proven” by one study or which pro­motes one solu­tion for all peo­ple is most like­ly over­stat­ing the case. Sweep­ing gen­er­al­iza­tions of any kind often indi­cate a lack of humil­i­ty that should be a red flag to read­ers. A study may very well “sug­gest” a cer­tain con­clu­sion but it rarely, if ever, “proves” it.

This is why we use a lot of cau­tious, hedg­ing lan­guage in Greater Good, like “might” or “implies.”

8. Is there any conflict of interest suggested by the funding or the researchers’ affiliations?

A recent study found that you could drink lots of sug­ary bev­er­ages with­out fear of get­ting fat, as long as you exer­cised. The fun­der? Coca Cola, which eager­ly pro­mot­ed the results. This doesn’t mean the results are wrong. But it does sug­gest you should seek a sec­ond opin­ion.

9. Does the researcher seem to have an agenda?

Read­ers could under­stand­ably be skep­ti­cal of mind­ful­ness med­i­ta­tion stud­ies pro­mot­ed by prac­tic­ing Bud­dhists or exper­i­ments on the val­ue of prayer con­duct­ed by Chris­tians. Again, it doesn’t auto­mat­i­cal­ly mean that the con­clu­sions are wrong. It does, how­ev­er, raise the bar for peer review and repli­ca­tion. For exam­ple, it took hun­dreds of exper­i­ments before we could begin say­ing with con­fi­dence that mind­ful­ness can indeed reduce stress.

10. Do the researchers acknowledge limitations and entertain alternative explanations?

Is the study focused on only one side of the sto­ry or one inter­pre­ta­tion of the data? Has it failed to con­sid­er or refute alter­na­tive expla­na­tions? Do they demon­strate aware­ness of which ques­tions are answered and which aren’t by their meth­ods?

I sum­ma­rize my per­son­al stance as a non-sci­en­tist toward sci­en­tif­ic find­ings as this: Curi­ous, but skep­ti­cal. I take it all seri­ous­ly and I take it all with a grain of salt. I judge it against my expe­ri­ence, know­ing that my expe­ri­ence cre­ates bias. I try to cul­ti­vate humil­i­ty, doubt, and patience. I don’t always suc­ceed; when I fail, I try to admit fault and for­give myself. My own under­stand­ing is imper­fect, and I remind myself that one study is only one step in under­stand­ing. Above all, I try to bear in mind that sci­ence is a process, and that con­clu­sions always raise more ques­tions for us to answer.

Jeremy Adam Smith– Jere­my Adam Smith is pro­duc­er and edi­tor of Greater Good, an online mag­a­zine based at UC-Berke­ley that high­lights ground break­ing sci­en­tific research into the roots of com­pas­sion and altru­ism. He is also the author or coed­i­tor of four books, includ­ing The Dad­dy Shift, Are We Born Racist?, and The Com­pas­sion­ate Instinct. Before join­ing the GGSC, Jere­my was a 2010-11 John S. Knight Jour­nal­ism Fel­low at Stan­ford Uni­ver­si­ty. Pub­lished here by cour­tesy of Greater Good.

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