I don't want to be anal about this but...in regards to the post below, I think a few clarifications need to be made.
First of all, scientific data is "suppressed" all the time. In fact, it's the norm. There are several reasons for this.
The number one reason is that precious little gets published unless you can show statistically significant results. For instance, if I tested oreos and chip's ahoy cookies in terms of weight gain, if there was no difference, nobody would be interested. But IF I showed that that the same caloric intake of oreos resulted in statistically significant wt gain compared to the same caloric intake of chip's ahoy, that would be publishable. Thus, the norm is to suppress nonsignificant results.
From a scientific point of view -- given the process -- if there is no valid, statistical difference between two knives, no one would care. Those results would never get past peer review UNLESS somehow you could make a compelling case for their importance. This CAN be done but infrequently.
Why is this the case? Publishing scientific information is expensive. So cost is one factor. Also, scientific information grows at an exponential rate. So usually only the most thoroughly tested significant results get published. Scientists don't have time to waste reading papers on tests that show nothing.
This might not be the BEST way to do things but this is how it is done in realiity.
Also, even many (most?) statistically significant results can, for all practical purposes, be meaningless. Therefore, there is a lot of science that gets published that has little practical significance. Calcium lowers blood pressure? By one psi. Detected because the sample size was 1000 participants in each each test group.
One knife cuts through nails better than another? Is this significant? Depends on how you plan on using the knife.
I kind of agree with the spirit of the post below but not necessarily the mechanics.
Hoodoo
>The vital principle is a scientist does not suppress data, no matter how clear he
thinks it is that data doesn't mean anything. Why not? Because it's not for him to
judge, all by himself with no peer review. A scientist is not afraid to publish data he
believes to be invalid and explain why it's invalid and trust other scientists to
understand that, or prove he's wrong about it. When everything is out in the open
and everybody has a chance to assess the results of experiments and discuss what
they mean, the truth emerges -- the scientific community is the free marketplace of ideas. (Ideally, anyway ... of course in the real world scientists are human beings
etc. etc., but that's the way it's supposed to be.)
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Hoodoo
Doubt grows with knowledge.
--Goethe
