Science | Page 26

Confirmation Bias
Confirmation bias is the tendency to look for information that supports one ’ s preconceptions . It impacts how we gather , interpret , and recall information – for example , when a person gives more weight to evidence that confirms their beliefs and undervalues evidence that could disprove it .
One of the early demonstrations of confirmation bias appeared in an experiment by Peter Watson in which the subjects were told to find a rule by which a series of numbers were generated . 5 This simple study showed that subjects chose responses that supported their hypotheses while rejecting contradictory evidence even though their hypotheses were not correct .
More recently , a study observed 6 researchers as they examined 165 lab experiments . In 88 % of cases in which results did not align with expectations , the scientists blamed the inconsistencies on how the experiments were conducted , rather than on their own theories . Consistent results , by contrast , were given little to no scrutiny . This particular bias is sometimes known as asymmetric attention .
As magicians – we love to exploit confirmation bias . For example , audiences will typically assume basic things like decks of cards are ‘ normal ’ and that dice aren ’ t loaded . In designing effects , magicians often bake in
‘ provers ’ that seemingly validate for the audience that

26 their assumptions are correct . Once the audience has been assured that their assumptions are correct , magicians have set the boundaries of an audience ’ s perception and awareness and are free to make the impossible happen . While this is fantastic for magic – it is extremely dangerous for science .

Fortunately , the study of ways to combat confirmation bias has drawn much attention in the scientific community over the past decade . 7 One approach , known as ‘ strong inference ,’ 8 suggests scientists make themselves explicitly list alternative explanations for their observations and debate the support for both the primary and competing hypotheses . An alternate approach gaining traction is known as blind data analysis . The general concept is to separate the data analyzers from the hypothesis generators . Under these circumstances , analyzers couldn ’ t possibly over-interpret the data , as they have nothing to unconsciously look for .
5 . P . C . Wason ( 1960 ) On the failure to eliminate hypotheses in a conceptual task , Quarterly Journal of Experimental Psychology , 12:3 , 129-140 , DOI : 10.1080 / 17470216008416717 6 . Fugelsang , J . A ., Stein , C . B ., Green , A . E . & amp ; Dunbar , K . N . Can . J . Exp . Psychol . 58 , 86 – 95 ( 2004 ). 7 . How scientists fool themselves – and how they can stop . Regina Nuzzo Nature volume 526 , pages 182 – 185 ( 2015 ) 8 . Platt , J . R . Science 146 , 347 – 353 ( 1964 ).
I believe that deeper awareness and conversation around how effectively the magic community uses assumption blindness could help the scientific community develop new and more effective approaches to combat it .
Closing thoughts — bringing magic and science back together
My experiences as a magician have shown me both how malleable perception is , and how prevalent cognitive gaps and biases are . While , as a performer , I greatly enjoy taking advantage of these gaps and biases to delight audiences , I ’ ve become acutely aware of how these same biases may be impacting my scientific studies .
Ultimately the goal of science is to build a sufficient understanding of the world so that we can derive models that effectively predict future unknowns , such as what will happen when we drop a ball , or someone takes a cancer drug . To develop accurate , highly predictive models , we need to be able to observe and interpret the world around us as accurately as possible . These days , I try to make sure to look at my science through the lens of a magician – examining all the ways I might be fooling myself . This inquiry has become a routine part of everything from how I design experiments to how I analyze data , and I believe this has made me a better , and more cautious , scientist .
Magic lives between the improbable , impossible , and inexplicable .
In addition to examining biases , there are many other ways in which scientists might learn from magicians . Afterall , much like science , magic lives at the intersection between the improbable , the impossible , and the inexplicable . When developing a new magic trick , a magician often begins by thinking of something impossible . Next , they figure out a way to make it happen – or at least to make an audience perceive that it happened .
This willingness to wholeheartedly dive into the impossible , to not be afraid of it , and to explore utterly ludicrous explanations as part of the process , is something that scientists are often afraid of . Oftentimes we only allow ourselves a narrow range of explanations . For an educated and adept magician , no explanation ( or possibility ) is too hard , too complex , or too impossible to consider . Perhaps more interactions between scientists and magicians will embolden scientists to dream a little bigger , to explore a wide range of possibilities and hypotheses , and , ultimately , to accelerate the progress of science .
Arthur C . Clarke said it well in his second law – “ The only way of discovering the limits of the possible is to venture a little way past them into the impossible .” I hope one day to be able to look back at the synergies that emerge from renewed conversations between scientists and magicians and see how magicians contributed to a new golden age of science filled with amazing breakthroughs . ■