Page 175 - English for Writing Research Papers

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9.2 Recognize the importance of 'bad data'

Every good book on scientifi c writing highlights the importance of admitting your
limitations. Mario Livio, an astrophysicist at the Space Telescope Science Institute
in Baltimore (USA), has even written a whole book – Brilliant Blunders – on this
topic. His reason for doing so was:

… to correct the impression that scientific breakthroughs are purely success stories. . . . The

road to triumph [is] paved with blunders.
A 'blunder' is a huge mistake. To enable referees to judge whether you have made a
mistake or not, you should not hide any negative results. Be upfront (clear and hon-
est) about the limitations of your methods and approach.

In Why People Believe Weird Things , author Professor Michael Shermer writes:

In science, the value of negative findings – failures – cannot be overemphasized. Usually

they are not wanted, and often they are not published. But most of the time failures are how
we get close to truth. Honest scientists will readily admit their errors, but all scientists are
kept in line by the fact that their fellow scientist will publicize any attempt to fudge. Not
pseudo scientists. They ignore or rationalize failures, especially when exposed.

Dr. Donald Dearborn, of Bates College, comments:

Your results may be of importance to others even though they did not support your hypoth-
esis. Do not fall into the trap of thinking that results contrary to what you expected are
necessarily “bad data”. If you carried out the work well, they are simply your results and
need interpretation. Many important discoveries can be traced to “bad data”.

And finally, Linus Pauling, winner of two Nobel prizes and some of whose fi ndings

were later found to be majorly flawed by other scientists, is reported to have said:

Mistakes do no harm in science because there are lots of smart people out there who will
immediately spot a mistake and correct it. You can only make a fool of yourself and that
does no harm, except to your pride.

Negative data are frequently commented on in the Results ( 17.7 ) and Discussion
( 18.6 ).